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@article{Sibuya1960,
author = {Sibuya, M.},
title = {Bivariate extreme statistics},
journal = {Annals of the Institute of Statistical Mathematics},
year = {1960},
volume = {11},
pages = {195 -- 210},
}

@article{Thibaud2015,
author = {Thibaud, Emeric and Aalto, Juha and Cooley, Daniel S. and Davison, Anthony C. and Heikkinen, Juha},
title = {Bayesian inference for the Brown-Resnick process, with an application to extreme low temperatures},
journal = {},
year = {2015},
archivePrefix = {arXiv},
eprint = {1506.07836}
}

@article{Lee1999,
author = {Lee, Daniel D. and Seung, Sebastian H.},
journal = {Nature},
year = {1999},
title = {Learning the parts of objects by non-negative matrix factorizations},
volume = {401},
pages = {788 -- 791}
}

@article{Mairal2014,
author = {Mairal, J. and Bach, F. and Ponce, J.},
journal = {Foundations and Trends in Computer Graphics and Vision},
title = {Sparse modeling for image and vision processing},
volume = {8},
pages = {85 -- 283},
year = {2014},
}

@article{Morris2016,
author = {Morris, Samuel A. and Reich, Brian J. and Thibaud, Emeric and Cooley, Daniel},
title = {A space-time skew-\emph{t} model for threshold exceedances},
journal = {Biometrics},
year = {under review}
}

@book{Diggle2007,
author = {Diggle, P and Ribeiro, P J},
isbn = {9780387485362},
publisher = {Springer New York},
series = {Springer Series in Statistics},
title = {{Model-based Geostatistics}},
year = {2007}
}

@book{Agresti2003,
author = {Agresti, A},
edition = {2nd},
file = {:Users/samorris/Dropbox/Mendeley/Unknown/Agresti - 2003 - Categorical Data Analysis.pdf:pdf},
isbn = {9780471458760},
publisher = {Wiley},
series = {Wiley Series in Probability and Statistics},
title = {{Categorical Data Analysis}},
year = {2003}
}

@article{DeOliveira2000,
abstract = {This work provides a framework to perform prediction in some types of binary random fields. It is assumed the binary random field is obtained by clipping a Gaussian random field at a fixed level. The model, following a Bayesian approach, is used to map a binary outcome over a bounded region D of the plane: For each location s0 $\epsilon$ D, we compute the optimal predictor of Z(s0), 0 or 1, given the binary data from a realization of the random field, and provide measures of prediction uncertainty amenable for binary outcomes. The optimal predictor and the measure of prediction uncertainty are computed through data augmentation using Markov Chain Monte Carlo methods; a less computationally demanding plug-in approach is also described. A brief description of a geostatistical method called indicator kriging is given as well as some of its shortcomings. The prediction ability of the model is illustrated with two simulated binary maps, obtaining satisfactory results, and comparisons between the Bayesian, plug-in, and indicator kriging approaches are given. (C) 2000 Elsevier Science B.V. All rights reserved.},
author = {{De Oliveira}, Victor},
doi = {10.1016/S0167-9473(99)00103-6},
file = {:Users/samorris/Dropbox/Mendeley/Computational Statistics and Data Analysis/De Oliveira - 2000 - Bayesian prediction of clipped Gaussian random fields.pdf:pdf},
issn = {01679473},
journal = {Computational Statistics and Data Analysis},
keywords = {Binary map,Binary random field,Data augmentation,Indicator kriging,Semivariogram,Spatial prediction},
number = {3},
pages = {299--314},
title = {{Bayesian prediction of clipped Gaussian random fields}},
volume = {34},
year = {2000}
}

@article{Finley2015,
author = {Finley, Andrew O. and Banerjee, Sudipto and Gelfand, Alan E.},
file = {:Users/samorris/Dropbox/Mendeley/Journal of Statistical Software/Finley, Banerjee, Gelfand - 2015 - spBayes for Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models.pdf:pdf},
journal = {Journal of Statistical Software},
keywords = {gaussian predictive process,markov chain monte,multivariate,spatial,temporal},
number = {13},
title = {{spBayes for large univariate and multivariate point-referenced spatio-remporal data models}},
volume = {63},
year = {2015}
}

@incollection{Smith2012,
author = {Smith, David R. and Yuancai, Lei and Walter, Christopher A. and Young, John A.},
booktitle = {Design and Analysis of Long-term Ecological Monitoring Studies},
chapter = {17},
editor = {Gitzen, Robert A. and Millspaugh, Joshua J. and Cooper, Andrew B. and Licht, Daniel S.},
isbn = {9781139510196},
pages = {381--396},
publisher = {Cambridge University Press},
title = {{Incorporating predicted species distribution in adaptive and conventional sampling designs}},
year = {2012}
}

@article{Pacifici2016,
abstract = {* Spatially clustered populations create unique challenges for conservation monitoring programmes. Advances in methodology typically are focused on either the design or the modelling stage of the study but do not involve integration of both. * We integrate adaptive cluster sampling and spatial occupancy modelling by developing two models to handle the dependence induced by cluster sampling. We compare these models to scenarios using simple random sampling and traditional occupancy models via simulation and data collected on a rare plant species, Tamarix ramosissima, found in China. * Our simulations show a marked improvement in confidence interval coverage for the new models combined with cluster sampling compared to simple random sampling and traditional occupancy models, with greatest improvement in the presence of low detection probability and spatial correlation in occupancy. * Accounting for the design using the simple cluster random-effects model reduces bias considerably, and full spatial modelling reduces bias further, especially for large n when the spatial covariance parameters can be estimated reliably. Both new models build on the strength of occupancy modelling and adaptive sampling and perform at least as well, and often better, than occupancy modelling alone. * We believe our approach is unique and potentially useful for a variety of studies directed at patchily distributed, clustered or rare species exhibiting spatial variation.},
author = {Pacifici, Krishna and Reich, Brian J. and Dorazio, Robert M. and Conroy, Michael J.},
doi = {10.1111/2041-210X.12499},
editor = {McPherson, Jana},
file = {:Users/samorris/Dropbox/Mendeley/Methods in Ecology and Evolution/Pacifici et al. - 2016 - Occupancy estimation for rare species using a spatially-adaptive sampling design.pdf:pdf},
issn = {2041210X},
journal = {Methods in Ecology and Evolution},
keywords = {Adaptive cluster sampling,Informative sampling,Probit regression,Rare species,Spatial regression,Tamarix ramosissima},
month = {mar},
number = {3},
pages = {285--293},
title = {{Occupancy estimation for rare species using a spatially-adaptive sampling design}},
volume = {7},
year = {2016}
}

@article{Sing2005,
abstract = {ROCR is a package for evaluating and visualizing the performance of scoring classifiers in the statistical language R. It features over 25 performance measures that can be freely combined to create two-dimensional performance curves. Standard methods for investigating trade-offs between specific performance measures are available within a uniform framework, including receiver operating characteristic (ROC) graphs, precision/recall plots, lift charts and cost curves. ROCR integrates tightly with R's powerful graphics capabilities, thus allowing for highly adjustable plots. Being equipped with only three commands and reasonable default values for optional parameters, ROCR combines flexibility with ease of usage. AVAILABILITY: http://rocr.bioinf.mpi-sb.mpg.de. ROCR can be used under the terms of the GNU General Public License. Running within R, it is platform-independent. CONTACT: tobias.sing@mpi-sb.mpg.de.},
author = {Sing, Tobias and Sander, Oliver and Beerenwinkel, Niko and Lengauer, Thomas},
doi = {10.1093/bioinformatics/bti623},
file = {:Users/samorris/Dropbox/Mendeley/Bioinformatics/Sing et al. - 2005 - ROCR visualizing classifier performance in R.pdf:pdf},
isbn = {1367-4803 (Print)$\backslash$n1367-4803 (Linking)},
issn = {1367-4803},
journal = {Bioinformatics},
month = {oct},
number = {20},
pages = {3940--3941},
pmid = {16096348},
title = {{ROCR: Visualizing classifier performance in R}},
volume = {21},
year = {2005}
}

@article{Fawcett2006,
abstract = {Network technologies have facilitated the implementation of health services based on ubiquitous systems, allowing pervasive monitoring of patients in their daily activities without significantly interfering in their lifestyle. This event entails the need to ensure adequate management and security of healthcare environment networks. However, traffic monitoring became an arduous work, requiring autonomic mechanisms to describe the network's normal behavior. Thus, Digital Signature of Network Segment using Flow analysis(DSNSF) as a mechanism to assist the networks management through traffic characterization is introduced. For this purpose, three methods belonging to different groups of algorithms are used: the statistical procedure Principal Component Analysis (PCA), the Ant Colony Optimization (ACO) metaheuristic and Holt'Winters forecasting method. These methods characterize the traffic into two distinct levels. The first one is the network infrastructure, which encompasses the entire network, including non-healthcare data from different sectors which compose an e-health environment. Profile creation about traffic used for monitoring of patients' vital and behavioral signs identifies the second level. Also, an approach for anomaly detection is proposed, which is able to recognize unusual events that may affect the proper operation of the services provided by the network.},
author = {Fawcett, Tom},
doi = {10.1016/j.patrec.2005.10.010},
file = {:Users/samorris/Dropbox/Mendeley/Pattern Recognition Letters/Fawcett - 2006 - An introduction to ROC analysis.pdf:pdf},
isbn = {01678655},
issn = {01678655},
journal = {Pattern Recognition Letters},
keywords = {classifier evaluation,evaluation metrics,roc analysis},
month = {jun},
number = {8},
pages = {861--874},
pmid = {9820922},
title = {{An introduction to ROC analysis}},
volume = {27},
year = {2006}
}

@article{Heagerty1998,
abstract = {Conventional geostatistics addresses the problem of estimation and prediction for continuous observations. But in many practical applications in public health, environmental remediation, or ecological research the most commonly available data are in the form of counts (e.g., number of cases) or indicator variables denoting above or below threshold values. Also, in many situations it is less expensive to obtain an imprecise categorical observation than to obtain precise measurements of the variable of interest (such as a contaminant). This article proposes a computationally simple method for estimation and prediction using binary or indicator data in space. The proposed method is based on pairwise likelihood contributions, and the large-sample properties of the estimators are obtained in a straightforward manner. We illustrate the methodology through application to indicator data related to gypsy moth defoliation in Massachusetts.},
author = {Heagerty, Patrick and Lele, Subhash},
doi = {10.1080/01621459.1998.10473771},
file = {:Users/samorris/Dropbox/Mendeley/Journal of the American Statistical Association/Heagerty, Lele - 1998 - A Composite Likelihood Approach to Binary Spatial Data.pdf:pdf},
isbn = {0162-1459},
issn = {0162-1459},
journal = {Journal of the American Statistical Association},
keywords = {empirical bayes,estimating function,hierarchical model,indicator kriging,iterated conditional modes,latent},
number = {September},
pages = {1099--1111},
title = {{A Composite likelihood approach to binary spatial data}},
volume = {1459},
year = {1998}
}

@Manual{Rmanual,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2016},
    url = {https://www.R-project.org/},
  }

@article{Toggweiler2001,
abstract = {The circulation of the ocean is usually divided into two parts, a wind-driven circulation that dominates in the upper few hundred meters, and a density-driven circulation that dominates below. The latter is called the ‘thermohaline' circulation because of the role of heating, cool- ing, freshening, and salinification in producing regional density differences within the ocean. The thermohaline circulation, for the most part, is an ‘overturning' circulation in which warm water flows poleward near the surface and is subsequently converted into cold water that sinks and flows equatorward in the in- terior. Radiocarbon measurements show that the thermohaline circulation turns over all the deep water in the ocean every 600 years or so},
author = {Hannachi, A. and Jolliffe, I. T. and Stephenson, D. B.},
file = {:Users/samorris/Dropbox/Mendeley/International Journal of Climatology/Hannachi, Jolliffe, Stephenson - 2007 - Empirical orthogonal functions and related techniques in atmospheric science A review.pdf:pdf},
isbn = {0262194929},
issn = {08998418},
journal = {International Journal of Climatology},
keywords = {Antarctic Circumpolar,Current,Ekman layer,Gulfstream,cline,conveyor,deep-water,formation,heat transport,hydrological cycle,pcyno-,salinity,thermocline,upwelling},
month = {jul},
number = {9},
pages = {1119--1152},
title = {{Empirical orthogonal functions and related techniques in atmospheric science: A review}},
volume = {27},
year = {2007},
}

@incollection{Everitt2008,
abstract = {Instructional Book for using R for programming},
address = {New York, NY},
author = {Everitt, Brian and Hothorn, Torsten},
booktitle = {An Introduction to Applied Multivariate Analysis with R},
doi = {10.1007/978-0-387-78171-6},
file = {:Users/samorris/Dropbox/Mendeley/An introduction to applied multivariate analysis with R/Everitt, Hothorn - 2008 - Principal Components Analysis.pdf:pdf},
isbn = {978-0-387-78170-9},
issn = {9780387938363},
pages = {21--54},
pmid = {22057480},
publisher = {Springer New York},
title = {{Principal components analysis}},
year = {2008}
}

@article{Genton2011,
author = {Genton, Marc G. and Ma, Yanyuan and Sang, Huiyan},
doi = {10.1093/biomet/asr020},
file = {:Users/samorris/Dropbox/Mendeley/Biometrika/Genton, Ma, Sang - 2011 - On the likelihood function of Gaussian max-stable processes.pdf:pdf},
issn = {00063444},
journal = {Biometrika},
keywords = {Composite likelihood,Extreme event,Multivariate index,Pairwise and triplewise inference,Spatial statistics},
number = {2},
pages = {481--488},
title = {{On the likelihood function of Gaussian max-stable processes}},
volume = {98},
year = {2011}
}

@article{deHaan1984,
abstract = {The elements of an arbitrary max-stable sequence are exhibited as functionals of a 2-dimensional Poisson point process. The result is extended to a continuous time max-stable process that is continuous in probability. We define an analogue of a stochastic integral appropriate for this context.},
author = {de Haan, L},
doi = {10.2307/2243357},
file = {:Users/samorris/Dropbox/Mendeley/The Annals of Probability/de Haan - 1984 - A Spectral Representation for Max-stable Processes.pdf:pdf},
isbn = {00911798},
issn = {0091-1798},
journal = {The Annals of Probability},
number = {4},
pages = {1194--1204},
title = {{A Spectral representation for max-stable processes}},
volume = {12},
year = {1984}
}

@article{Wang2011,
abstract = {Max-stable random fields play a central role in modeling extreme value phenomena. We obtain an explicit formula for the conditional probability in general max-linear models, which include a large class of max-stable random fields. As a consequence, we develop an algorithm for efficient and exact sampling from the conditional distributions. Our method provides a computational solution to the prediction problem for spectrally discrete max-stable random fields. This work offers new tools and a new perspective to many statistical inference problems for spatial extremes, arising, for example, in meteorology, geology, and environmental applications.},
author = {Wang, Yizao and Stoev, Stilian A.},
doi = {10.1239/aap/1308662488},
isbn = {00018678},
issn = {00018678},
journal = {Advances in Applied Probability},
keywords = {MARMA process,Max-stable process,Regular conditional probability,Set cover problem,Smith model,Spectrally discrete representation},
number = {2},
pages = {461--483},
title = {{Conditional sampling for spectrally discrete max-stable random fields}},
volume = {43},
year = {2011},
archivePrefix = {arXiv},
eprint = {1005.0312v2}
}

@article{Kim2004,
abstract = {A model based on the skew Gaussian distribution is presented to handle skewed spatial data. It extends the results of popular Gaussian process models. Markov chain Monte Carlo techniques are used to generate samples from the posterior distributions of the parameters. Finally, this model is applied in the spatial prediction of weekly rainfall. Cross-validation shows that the predictive performance of our model compares favorably with several kriging variants. {\textcopyright} 2002 Elsevier B.V. All rights reserved.},
author = {Kim, Hyoung Moon and Mallick, Bani K.},
doi = {10.1016/S0378-3758(02)00501-3},
file = {:Users/samorris/Dropbox/Mendeley/Journal of Statistical Planning and Inference/Kim, Mallick - 2004 - A Bayesian prediction using the skew Gaussian distribution.pdf:pdf},
issn = {03783758},
journal = {Journal of Statistical Planning and Inference},
keywords = {Cross-validation,Kriging,Markov chain Monte Carlo,Non-Gaussian characteristics},
number = {1-2},
pages = {85--101},
title = {{A Bayesian prediction using the skew Gaussian distribution}},
volume = {120},
year = {2004}
}

@Manual{Ribatet2015,
    title = {SpatialExtremes: Modelling Spatial Extremes},
    author = {Mathieu Ribatet},
    year = {2015},
    note = {R package version 2.0-2},
    url = {https://CRAN.R-project.org/package=SpatialExtremes},
  }

@Article{Stephenson2002,
	title = {evd: Extreme Value Distributions},
	author = {A. G. Stephenson},
	journal = {R News},
	year = {2002},
	volume = {2},
	number = {2},
	pages = {0},
	month = {June},
}

@article{Kabluchko2009,
abstract = {Let Wi, i∈ℕ, be independent copies of a zero-mean Gaussian process {\{}W(t), t∈ℝd{\}} with stationary increments and variance $\sigma$2(t). Independently of Wi, let ∑i=1∞$\delta$Ui be a Poisson point process on the real line with intensity e−y dy. We show that the law of the random family of functions {\{}Vi(⋅), i∈ℕ{\}}, where Vi(t)=Ui+Wi(t)−$\sigma$2(t)/2, is translation invariant. In particular, the process $\eta$(t)=⋁i=1∞Vi(t) is a stationary max-stable process with standard Gumbel margins. The process $\eta$ arises as a limit of a suitably normalized and rescaled pointwise maximum of n i.i.d. stationary Gaussian processes as n→∞ if and only if W is a (nonisotropic) fractional Brownian motion on ℝd. Under suitable conditions on W, the process $\eta$ has a mixed moving maxima representation.},
author = {Kabluchko, Zakhar and Schlather, Martin and de Haan, Laurens},
doi = {10.1214/09-AOP455},
issn = {00911798},
journal = {Annals of Probability},
keywords = {Extremes,Gaussian processes,Poisson point processes,Stationary max-stable processes},
number = {5},
pages = {2042--2065},
title = {{Stationary max-stable fields associated to negative definite functions}},
volume = {37},
year = {2009},
archivePrefix = {arXiv},
eprint = {0806.2780v3},
}

@book{Gelfand2010,
	Author = {Gelfand, A.E. and Diggle, P. and Guttorp, P. and Fuentes, M.},
	Isbn = {9781420072884},
	Lccn = {2010006620},
	Publisher = {CRC Press},
	Series = {Chapman \& Hall/CRC Handbooks of Modern Statistical Methods},
	Title = {Handbook of Spatial Statistics},
	Year = {2010}}

@incollection{Cooley2006,
	Address = {New York, NY},
	Author = {Cooley, Daniel and Naveau, Philippe and Poncet, Paul},
	Booktitle = {Dependence in Probability and Statistics},
	Chapter = {Variograms},
	Date-Modified = {2016-06-27 19:37:59 +0000},
	Editor = {Bertail, Patrice and Soulier, Philippe and Doukhan, Paul},
	Isbn = {978-0-387-31741-0},
	Pages = {373--390},
	Publisher = {Springer New York},
	Series = {Lecture Notes in Statistics},
	Title = {{Variograms for spatial max-stable random fields}},
	Volume = {187},
	Year = {2006},
	Bdsk-Url-1 = {http://dx.doi.org/10.1007/0-387-36062-X}}

@article{Albert1993,
	Abstract = {A vast literature in statistics, biometrics, and econometrics is concerned with the analysis of binary and polychotomous response data. The classical approach fits a categorical response regression model using maximum likelihood, and inferences about the model are based on the associated asymptotic theory. The accuracy of classical confidence statements is questionable for small sample sizes. In this article, exact Bayesian methods for modeling categorical response data are developed using the idea of data augmentation. The general approach can be summarized as follows. The probit regression model for binary outcomes is seen to have an underlying normal regression structure on latent continuous data. Values of the latent data can be simulated from suitable truncated normal distributions. If the latent data are known, then the posterior distribution of the parameters can be computed using standard results for normal linear models. Draws from this posterior are used to sample new latent data, and the process is iterated with Gibbs sampling. This data augmentation approach provides a general framework for analyzing binary regression models. It leads to the same simplification achieved earlier for censored regression models. Under the proposed framework, the class of probit regression models can be enlarged by using mixtures of normal distributions to model the latent data. In this normal mixture class, one can investigate the sensitivity of the parameter estimates to the choice of "link function," which relates the linear regression estimate to the fitted probabilities. In addition, this approach allows one to easily fit Bayesian hierarchical models. One specific model considered here reflects the belief that the vector of regression coefficients lies on a smaller dimension linear subspace. The methods can also be generalized to multinomial response models with {\$}J {\textgreater} 2{\$} categories. In the ordered multinomial model, the J categories are ordered and a model is written linking the cumulative response probabilities with the linear regression structure. In the unordered multinomial model, the latent variables have a multivariate normal distribution with unknown variance-covariance matrix. For both multinomial models, the data augmentation method combined with Gibbs sampling is outlined. This approach is especially attractive for the multivariate probit model, where calculating the likelihood can be difficult.},
	Author = {Albert, Jim and Chib, Siddhartha},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of the American Statistical Association/Albert, Chib - 1993 - Bayesian Analysis of Binary and Polychotomous Response Data.pdf:pdf},
	Isbn = {01621459},
	Issn = {{\textless}null{\textgreater}},
	Journal = {Journal of the American Statistical Association},
	Keywords = {binary probit,data augmentation,gibbs sampling,hierarchical bayes modeling,latent data,logit model,mul-,residual analysis,student-r link function,tinomial probit},
	Number = {422},
	Pages = {669--679},
	Title = {{Bayesian Analysis of Binary and Polychotomous Response Data}},
	Volume = {88},
	Year = {1993},
}

@article{Allard2007,
	Author = {Allard, Denis and Naveau, Philippe},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Communications in Statistics - Theory and Methods/Allard, Naveau - 2007 - A New Spatial Skew-Normal Random Field Model.pdf:pdf},
	Issn = {0361-0926},
	Journal = {Communications in Statistics - Theory and Methods},
	Keywords = {multivariate},
	Month = {jul},
	Number = {9},
	Pages = {1821--1834},
	Title = {{A New Spatial Skew-Normal Random Field Model}},
	Volume = {36},
	Year = {2007},
	Bdsk-Url-1 = {http://www.tandfonline.com/doi/abs/10.1080/03610920601126290},
	Bdsk-Url-2 = {http://dx.doi.org/10.1080/03610920601126290}}

@article{ArellanoValle2008,
	Abstract = {For statistical inference connected to the scalar skew-normal distribution, it is known that the so-called centred parametrization provides a more convenient parametrization than the one commonly employed for writing the density function. We extend the definition of the centred parametrization to the multivariate case, and study the corresponding information matrix. ?? 2008 Elsevier Inc. All rights reserved.},
	Author = {Arellano-Valle, Reinaldo B. and Azzalini, Adelchi},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of Multivariate Analysis/Arellano-Valle, Azzalini - 2008 - The centred parametrization for the multivariate skew-normal distribution.pdf:pdf},
	Isbn = {0047-259X},
	Issn = {0047259X},
	Journal = {Journal of Multivariate Analysis},
	Keywords = {62E20,62F12,62H12,Index of skewness,Parametrization,Singular information matrix,Skew-normal distribution},
	Number = {7},
	Pages = {1362--1382},
	Title = {{The centred parametrization for the multivariate skew-normal distribution}},
	Volume = {99},
	Year = {2008},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.jmva.2008.01.020}}

@book{Azzalini2014,
	Author = {Azzalini, A. and Capitanio, A.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	Isbn = {9781107029279},
	Publisher = {Cambridge University Press},
	Series = {Institute of Mathematical Statistics Monographs},
	Title = {{The Skew-Normal and Related Families}},
	Year = {2014},
	Bdsk-Url-1 = {https://books.google.com/books?id=-tkaAgAAQBAJ}}

@article{Azzalini1999,
	Author = {Azzalini, A. and Capitanio, A.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of the Royal Statistical Society Series B (Statistical Methodology)/Azzalini, Capitanio - 1999 - Statistical applications of the multivariate skew normal distribution.pdf:pdf},
	Issn = {1369-7412},
	Journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
	Keywords = {elliptical distributions,multivariate normal distribution,quadratic forms,skew normal distribution,skewness},
	Month = {aug},
	Number = {3},
	Pages = {579--602},
	Title = {{Statistical applications of the multivariate skew normal distribution}},
	Volume = {61},
	Year = {1999},
	Bdsk-Url-1 = {http://doi.wiley.com/10.1111/1467-9868.00194},
	Bdsk-Url-2 = {http://dx.doi.org/10.1111/1467-9868.00194}}

@article{Azzalini1996,
	Author = {Azzalini, A. and {Dalla Valle}, A.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Biometrika/Azzalini, Dalla Valle - 1996 - The multivariate skew-normal distribution.pdf:pdf},
	Issn = {0006-3444},
	Journal = {Biometrika},
	Month = {dec},
	Number = {4},
	Pages = {715--726},
	Title = {{The multivariate skew-normal distribution}},
	Volume = {83},
	Year = {1996},
	Bdsk-Url-1 = {http://biomet.oxfordjournals.org/content/83/4/715.short%20http://biomet.oupjournals.org/cgi/doi/10.1093/biomet/83.4.715},
	Bdsk-Url-2 = {http://dx.doi.org/10.1093/biomet/83.4.715}}

@article{Azzalini2005,
	Author = {Azzalini, Adelchi},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Scandinavian Journal of Statistics/Azzalini - 2005 - The Skew‐normal Distribution and Related Multivariate Families.pdf:pdf},
	Issn = {0303-6898},
	Journal = {Scandinavian Journal of Statistics},
	Keywords = {1,background and motivation,flexible parametric family,graphical models,heavy tails,hidden truncation model,selective sampling,skew- t distribution,skew-elliptical distribution,skew-normal distribution,stochastic frontier models},
	Month = {jun},
	Number = {2},
	Pages = {159--188},
	Title = {{The Skew‐normal Distribution and Related Multivariate Families}},
	Volume = {32},
	Year = {2005},
	Bdsk-Url-1 = {http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9469.2005.00426.x/abstract%20http://doi.wiley.com/10.1111/j.1467-9469.2005.00426.x},
	Bdsk-Url-2 = {http://dx.doi.org/10.1111/j.1467-9469.2005.00426.x}}

@article{Azzalini2003,
	Author = {Azzalini, Adelchi and Capitanio, Antonella},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of the Royal Statistical Society Series B (Statistical Methodology)/Azzalini, Capitanio - 2003 - Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t-distribution(2).pdf:pdf},
	Issn = {1369-7412},
	Journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
	Keywords = {asymmetry,central symmetry,elliptical distributions,healy,multivariate,quadratic forms,s plot,skew normal distribution,skewness,t -distribution},
	Month = {may},
	Number = {2},
	Pages = {367--389},
	Title = {{Distributions generated by perturbation of symmetry with emphasis on a multivariate skew \emph{t}-distribution}},
	Volume = {65},
	Year = {2003},
}

@article{Bacro2014,
	Abstract = {Parametric inference for spatial max-stable processes is difficult since the related likelihoods are unavailable. A likelihood approach based on the bivariate distribution of block maxima has been recently proposed. However modeling block maxima is a wasteful approach provided that other information is available. Moreover an approach based on block maxima, typically annual, is unable to take into account the fact that maxima occur or not simultaneously. If time series of, say, daily data are available, then estimation procedures based on exceedances of a high threshold could mitigate such problems. We focus on two approaches for composing likelihoods based on pairs of exceedances. The first one comes from the tail approximation for bivariate distribution proposed by Ledford and Tawn (Biometrika 83:169--187, 1996) when both pairs of observations exceed the fixed threshold. The second one uses the bivariate extension (Rootz{\'{e}}n and Tajvidi in Bernoulli 12:917--930, 2006) of the generalized Pareto distribution which allows to model exceedances when at least one of the components is over the threshold. The two approaches are compared through a simulation study where both processes in a domain of attraction of a max-stable process and max-stable processes are successively considered as time replications, according to different degrees of spatial dependency. Results put forward how the nature of the time replications influences the bias of estimations and highlight the choice of each approach regarding to the strength of the spatial dependencies and the threshold choice.},
	Author = {Bacro, Jean Noel and Gaetan, Carlo},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Statistics and Computing/Bacro, Gaetan - 2014 - Estimation of spatial max-stable models using threshold exceedances.pdf:pdf},
	Issn = {09603174},
	Journal = {Statistics and Computing},
	Keywords = {Composite likelihood,Extremal dependence,Generalized Pareto distribution,Spatial statistics},
	Pages = {651----662},
	Title = {{Estimation of spatial max-stable models using threshold exceedances}},
	Volume = {24},
	Year = {2014},
	archivePrefix = {arXiv},
	eprint = {1205.1107v1}
	}

@article{Banerjee2008,
	Abstract = {With scientific data available at geocoded locations, investigators are increasingly turning to spatial process models for carrying out statistical inference. Over the last decade, hierarchical models implemented through Markov chain Monte Carlo methods have become especially popular for spatial modelling, given their flexibility and power to fit models that would be infeasible with classical methods as well as their avoidance of possibly inappropriate asymptotics. However, fitting hierarchical spatial models often involves expensive matrix decompositions whose computational complexity increases in cubic order with the number of spatial locations, rendering such models infeasible for large spatial data sets. This computational burden is exacerbated in multivariate settings with several spatially dependent response variables. It is also aggravated when data are collected at frequent time points and spatiotemporal process models are used. With regard to this challenge, our contribution is to work with what we call predictive process models for spatial and spatiotemporal data. Every spatial (or spatiotemporal) process induces a predictive process model (in fact, arbitrarily many of them). The latter models project process realizations of the former to a lower dimensional subspace, thereby reducing the computational burden. Hence, we achieve the flexibility to accommodate non-stationary, non-Gaussian, possibly multivariate, possibly spatiotemporal processes in the context of large data sets. We discuss attractive theoretical properties of these predictive processes. We also provide a computational template encompassing these diverse settings. Finally, we illustrate the approach with simulated and real data sets.},
	Author = {Banerjee, Sudipto and Gelfand, Alan E. and Finley, Andrew O. and Sang, Huiyan},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of the Royal Statistical Society. Series B Statistical Methodology/Banerjee et al. - 2008 - Gaussian predictive process models for large spatial data sets.pdf:pdf},
	Issn = {13697412},
	Journal = {Journal of the Royal Statistical Society. Series B: Statistical Methodology},
	Keywords = {Co-regionalization,Gaussian processes,Hierarchical modelling,Kriging,Markov chain Monte Carlo methods,Multivariate spatial processes,Space-time processes},
	Pmid = {19750209},
	Title = {{Gaussian predictive process models for large spatial data sets}},
	Year = {2008},
	Bdsk-Url-1 = {http://dx.doi.org/10.1111/j.1467-9868.2008.00663.x}}

@article{Baringhaus2004,
	Author = {Baringhaus, L. and Franz, C.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of Multivariate Analysis/Baringhaus, Franz - 2004 - On a new multivariate two-sample test.pdf:pdf},
	Issn = {0047259X},
	Journal = {Journal of Multivariate Analysis},
	Keywords = {bootstrapping,multivariate two-sample test,orthogonal invariance,projection method},
	Month = {jan},
	Number = {1},
	Pages = {190--206},
	Title = {{On a new multivariate two-sample test}},
	Volume = {88},
	Year = {2004},
	Bdsk-Url-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0047259X03000794},
	Bdsk-Url-2 = {http://dx.doi.org/10.1016/S0047-259X(03)00079-4}}

@article{Begueria2006,
	Author = {Beguer{\'{i}}a, Santiago and Vicente-Serrano, Sergio M.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of Applied Meteorology and Climatology/Beguer{\'{i}}a, Vicente-Serrano - 2006 - Mapping the Hazard of Extreme Rainfall by Peaks over Threshold Extreme Value Analysis and Spatial(2).pdf:pdf},
	Issn = {1558-8424},
	Journal = {Journal of Applied Meteorology and Climatology},
	Month = {jan},
	Number = {1},
	Pages = {108--124},
	Title = {{Mapping the Hazard of Extreme Rainfall by Peaks over Threshold Extreme Value Analysis and Spatial Regression Techniques}},
	Volume = {45},
	Year = {2006},
	Bdsk-Url-1 = {http://journals.ametsoc.org/doi/abs/10.1175/JAM2324.1},
	Bdsk-Url-2 = {http://dx.doi.org/10.1175/JAM2324.1}}

@article{Beranger2016,
	Abstract = {Skew-symmetric families of distributions such as the skew-normal and skew-{\$}t{\$} represent supersets of the normal and {\$}t{\$} distributions, and they exhibit richer classes of extremal behaviour. By defining a non-stationary skew-normal process, which allows the easy handling of positive definite, non-stationary covariance functions, we derive a new family of max-stable processes - the extremal-skew-{\$}t{\$} process. This process is a superset of non-stationary processes that include the stationary extremal-{\$}t{\$} processes. We provide the spectral representation and the resulting angular densities of the extremal-skew-{\$}t{\$} process, and illustrate its practical implementation (Includes Supporting Information).},
	Author = {Beranger, Boris and Padoan, Simone A and Sisson, Scott A},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/ArXiv e-prints/Beranger, Padoan, Sisson - 2016 - Models for extremal dependence derived from skew-symmetric families.pdf:pdf},
	Journal = {},
	Keywords = {angular density,asymptotic independence,extremal coefficient,extreme values,max-stable distribution,non-central extended skew-t distribution,non-stationarity,skew-normal distribution,skew-normal process,skew-t distribution},
	Month = {jul},
	Title = {{Models for extremal dependence derived from skew-symmetric families}},
	Year = {2016},
	archivePrefix = {arXiv},
	eprint = {1507.00108},
	}

@article{Bermudez2001,
	Author = {Bermudez, P de Zea and Turkman, MAA and Turkman, KF},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Extremes/Bermudez, Turkman, Turkman - 2001 - A Predictive Approach to Tail Probability Estimation.pdf:pdf},
	Journal = {Extremes},
	Keywords = {62f15,ams 2000 subject classi,bayesian hierarchical models,cation,extreme value distribution,gibbs sampling,markov chain,monte carlo methods,peaks over threshold,primaryð60g70},
	Number = {4},
	Pages = {295--314},
	Title = {{A Predictive Approach to Tail Probability Estimation}},
	Volume = {4},
	Year = {2001},
}

@article{Bernard2013,
	Author = {Bernard, Elsa and Naveau, Philippe and Vrac, Mathieu and Mestre, Olivier},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of Climate/Bernard et al. - 2013 - Clustering of Maxima Spatial Dependencies among Heavy Rainfall in France.pdf:pdf},
	Issn = {0894-8755},
	Journal = {Journal of Climate},
	Month = {oct},
	Number = {20},
	Pages = {7929--7937},
	Title = {{Clustering of Maxima: Spatial Dependencies among Heavy Rainfall in France}},
	Volume = {26},
	Year = {2013},
}

@article{Blanchet2010,
	Author = {Blanchet, J. and Lehning, M.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Hydrology and Earth System Sciences/Blanchet, Lehning - 2010 - Mapping snow depth return levels smooth spatial modeling versus station interpolation.pdf:pdf},
	Isbn = {1425272010},
	Issn = {1607-7938},
	Journal = {Hydrology and Earth System Sciences},
	Month = {dec},
	Number = {12},
	Pages = {2527--2544},
	Title = {{Mapping snow depth return levels: smooth spatial modeling versus station interpolation}},
	Volume = {14},
	Year = {2010},
}

@article{Blanchet2011,
	Author = {Blanchet, Juliette and Davison, Anthony C.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/The Annals of Applied Statistics/Blanchet, Davison - 2011 - Spatial modeling of extreme snow depth.pdf:pdf},
	Issn = {1932-6157},
	Journal = {The Annals of Applied Statistics},
	Month = {sep},
	Number = {3},
	Pages = {1699--1725},
	Title = {{Spatial modeling of extreme snow depth}},
	Volume = {5},
	Year = {2011},
	archivePrefix = {arXiv},
	eprint = {1111.7091v1},
}

@article{Branco2001,
	Author = {Branco, M{\'{a}}rcia D. and Dey, Dipak K.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of Multivariate Analysis/Branco, Dey - 2001 - A General Class of Multivariate Skew-Elliptical Distributions.pdf:pdf},
	Issn = {0047259X},
	Journal = {Journal of Multivariate Analysis},
	Keywords = {and phrases,elliptical distributions,exponential power family,mixture,of normals,pearson type ii,skewness},
	Month = {oct},
	Number = {1},
	Pages = {99--113},
	Title = {{A general class of multivariate skew-elliptical distributions}},
	Volume = {79},
	Year = {2001}
}

@article{Brier1950,
	Author = {Brier, Glenn W.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Monthly Weather Review/Brier - 1950 - Verification of Forecasts Expressed in Terms of Probability.pdf:pdf},
	Issn = {0027-0644},
	Journal = {Monthly Weather Review},
	Month = {jan},
	Number = {1},
	Pages = {1--3},
	Title = {{Verification of Forecasts Expressed in Terms of Probability}},
	Volume = {78},
	Year = {1950},
}

@article{Brown1977,
	Abstract = {The maxima of independent Weiner processes spatially normalized with time scales compressed is considered and it is shown that a weak limit process exists. This limit process is stationary, and its one-dimensional distributions are of standard extreme-value type. The method of proof involves showing convergence of related point processes to a limit Poisson point process. The method is extended to handle the maxima of independent Ornstein-Uhlenbeck processes.},
	Author = {Brown, Bruce M and Resnick, Sidney I},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of Applied Probability/Brown, Resnick - 1977 - Extreme Values of Independent Stochastic Processes.pdf:pdf},
	Issn = {00219002},
	Journal = {Journal of Applied Probability},
	Number = {4},
	Pages = {pp. 732--739},
	Publisher = {Applied Probability Trust},
	Title = {{Extreme Values of Independent Stochastic Processes}},
	Volume = {14},
	Year = {1977}
}

@article{Cabras2012,
	Author = {Cabras, Stefano and Racugno, Walter and Castellanos, Mar{\'{i}}a Eugenia and Ventura, Laura},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Scandinavian Journal of Statistics/Cabras et al. - 2012 - A Matching Prior for the Shape Parameter of the Skew-Normal Distribution.pdf:pdf},
	Issn = {03036898},
	Journal = {Scandinavian Journal of Statistics},
	Keywords = {integrated likelihood,modified profile likelihood,monotone likelihood,nuisance,parameters,pseudo-likelihood,regression model,skewness},
	Month = {jun},
	Number = {2},
	Pages = {236--247},
	Title = {{A Matching Prior for the Shape Parameter of the Skew-Normal Distribution}},
	Volume = {39},
	Year = {2012},
}

@article{Chambers1976,
	Author = {Chambers, J. M. and Mallows, C. L. and Stuck, B. W.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of the American Statistical Association/Chambers, Mallows, Stuck - 1976 - A Method for Simulating Stable Random Variables.pdf:pdf},
	Issn = {0162-1459},
	Journal = {Journal of the American Statistical Association},
	Month = {jun},
	Number = {354},
	Pages = {340--344},
	Title = {{A Method for Simulating Stable Random Variables}},
	Volume = {71},
	Year = {1976},
}

@article{Clauset2013,
	Abstract = {Quantities with right-skewed distributions are ubiquitous in complex social systems, including political conflict, economics and social networks, and these systems sometimes produce extremely large events. For instance, the 9/11 terrorist events produced nearly 3000 fatalities, nearly six times more than the next largest event. But, was this enormous loss of life statistically unlikely given modern terrorism's historical record? Accurately estimating the probability of such an event is complicated by the large fluctuations in the empirical distribution's upper tail. We present a generic statistical algorithm for making such estimates, which combines semi-parametric models of tail behavior and a non-parametric bootstrap. Applied to a global database of terrorist events, we estimate the worldwide historical probability of observing at least one 9/11-sized or larger event since 1968 to be 11-35{\%}. These results are robust to conditioning on global variations in economic development, domestic versus international events, the type of weapon used and a truncated history that stops at 1998. We then use this procedure to make a data-driven statistical forecast of at least one similar event over the next decade.},
	Author = {Clauset, Aaron and Woodard, Ryan},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/The Annals of Applied Statistics/Clauset, Woodard - 2013 - Estimating the historical and future probabilities of large terrorist events.pdf:pdf},
	Journal = {The Annals of Applied Statistics},
	Month = {sep},
	Pages = {9},
	Title = {{Estimating the historical and future probabilities of large terrorist events}},
	Year = {2013},
	archivePrefix = {arXiv},
	eprint = {1209.0089}
}

@article{Cohen1960,
	Author = {Cohen, Jacob},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Educational and Psychological Measurement/Cohen - 1960 - A Coefficient of Agreement for Nominal Scales.pdf:pdf},
	Journal = {Educational and Psychological Measurement},
	Number = {1},
	Pages = {37--46},
	Title = {{A Coefficient of agreement for nominal scales}},
	Volume = {20},
	Year = {1960}
}

@book{Coles2001,
	Address = {London},
	Author = {Coles, Stuart},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	Isbn = {9781852334598},
	Publisher = {Springer},
	Series = {Lecture Notes in Control and Information Sciences},
	Title = {{An Introduction to Statistical Modeling of Extreme Values}},
	Year = {2001}
}

@article{Coles1991,
	Abstract = {The classical treatment of multivariate extreme values is through componentwise ordering, though in practice most interest is in actual extreme events. Here the point process of observations which are extreme in at least one component is considered. Parametric models for the dependence between components must satisfy certain constraints. Two new techniques for generating such models are presented. Aspects of the statistical estimation of the resulting models are discussed and are illustrated with an application to oceanographic data.},
	Author = {Coles, Stuart G. and Tawn, Jonathan A.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of the Royal Statistical Society Series B (Methodological)/Coles, Tawn - 1991 - Modelling Extreme Multivariate Events.pdf:pdf},
	Journal = {Journal of the Royal Statistical Society: Series B (Methodological)},
	Keywords = {extreme value theory,generalized pareto distribution,likelihood,maximum,multivariate ordering,non-homogeneous poisson process},
	Number = {2},
	Pages = {377--392},
	Title = {{Modelling Extreme Multivariate Events}},
	Volume = {53},
	Year = {1991}
}

@article{Coles1999,
	Abstract = {Quantifying dependence is a central theme in probabilistic and statistical methods for multivariate extreme values. Two situations are possible: one where, in a limiting sense, the extremes are dependent; the other where, in the same sense, the extremes are independent. This paper comprises an overview of the principal issues through a unified approach which encompasses both these situations. Novel diagnostic measures for dependence are also developed which provide complementary information about different aspects of extremal dependence. The paper is written in an elementary style, with the methodology illustrated by application to theoretical examples and typical data-sets. These data-sets and the S-plus functions used for the analyses are available online.},
	Author = {Coles, Stuart and Heffernan, Janet and Tawn, Jonathan},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Extremes/Coles, Heffernan, Tawn - 1999 - Dependence Measures for Extreme Value Analyses.pdf:pdf},
	Journal = {Extremes},
	Keywords = {asymptotic independence,bivariate extreme value distribution,copula,point processes},
	Number = {4},
	Pages = {339--365},
	Title = {{Dependence measures for extreme value analyses}},
	Volume = {2},
	Year = {1999}
}

@article{Cooley2012b,
	Author = {Cooley, D. and Sain, S. R.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Statistical Science/Cooley, Sain - 2012 - Discussion of ``Statistical Modeling of Spatial Extremes'' by A. C. Davison, S. A. Padoan and M. Ribatet.pdf:pdf},
	Issn = {0883-4237},
	Journal = {Statistical Science},
	Month = {may},
	Number = {2},
	Pages = {187--188},
	Title = {{Discussion of ``Statistical modeling of spatial extremes'' by A. C. Davison, S. A. Padoan and M. Ribatet}},
	Volume = {27},
	Year = {2012}
}

@article{Cooley2012,
	Abstract = {We survey the current practice of analyzing spatial extreme data, which lies at the intersection of extreme value theory and geostatistics. Characterizations of multivariate max-stable distributions typically assume specific univariate marginal distributions, and their statistical applications generally require capturing the tail behavior of the margins and describing the tail dependence among the components. We review current methodology for spatial extremes analysis, discuss the extension of the finite-dimensional extremes framework to spatial processes, review spatial dependence metrics for extremes, survey current modeling practice for the task of modeling marginal distributions, and then examine max-stable process models and copula approaches for modeling residual spatial dependence after accounting for marginal effects.},
	Author = {Cooley, Daniel and Cisewski, Jessi and Erhardt, Robert J. and Mannshardt, Elizabeth and Omolo, Bernard Oguna and Sun, Ying},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/REVSTAT/Cooley et al. - 2012 - A survey of spatial extremes Measuring spatial dependence and modeling spatial effects.pdf:pdf},
	Journal = {REVSTAT},
	Keywords = {copula,extremal coefficient,hierarchical model,madogram,max-stable process,multivarate extreme value distribution},
	Number = {1},
	Pages = {135--165},
	Title = {{A survey of spatial extremes: Measuring spatial dependence and modeling spatial effects}},
	Volume = {10},
	Year = {2012}
}

@article{Cooley2012a,
	Abstract = {Phenomena such as air pollution levels are of greatest interest when observations are large, but standard prediction methods are not specifically designed for large observations. We propose a method, rooted in extreme value theory, which approximates the conditional distribution of an unobserved component of a random vector given large observed values. Specifically, for Z=(Z1,{\ldots},Zd)T and Z−d=(Z1,{\ldots},Zd−1)T, the method approximates the conditional distribution of [Zd|Z−d=z−d] when ∥z−d∥{\textgreater}r∗. The approach is based on the assumption that Z is a multivariate regularly varying random vector of dimension d. The conditional distribution approximation relies on knowledge of the angular measure of Z, which provides explicit structure for dependence in the distribution's tail. As the method produces a predictive distribution rather than just a point predictor, one can answer any question posed about the quantity being predicted, and, in particular, one can assess how well the extreme behavior is represented. Using a fitted model for the angular measure, we apply our method to nitrogen dioxide measurements in metropolitan Washington DC. We obtain a predictive distribution for the air pollutant at a location given the air pollutant's measurements at four nearby locations and given that the norm of the vector of the observed measurements is large.},
	Author = {Cooley, Daniel and Davis, Richard A. and Naveau, Philippe},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/The Annals of Applied Statistics/Cooley, Davis, Naveau - 2012 - Approximating the conditional density given large observed values via a multivariate extremes framework,.pdf:pdf},
	Issn = {1932-6157},
	Journal = {The Annals of Applied Statistics},
	Month = {dec},
	Number = {4},
	Pages = {1406--1429},
	Title = {{Approximating the conditional density given large observed values via a multivariate extremes framework, with application to environmental data}},
	Volume = {6},
	Year = {2012}
}

@article{Cooley2007,
	Author = {Cooley, Daniel and Nychka, Douglas and Naveau, Philippe},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of the American Statistical Association/Cooley, Nychka, Naveau - 2007 - Bayesian Spatial Modeling of Extreme Precipitation Return Levels.pdf:pdf},
	Issn = {0162-1459},
	Journal = {Journal of the American Statistical Association},
	Keywords = {colorado,extreme value theory,generalized pareto distribution,hierarchical model,latent process},
	Month = {sep},
	Number = {479},
	Pages = {824--840},
	Title = {{Bayesian Spatial Modeling of Extreme Precipitation Return Levels}},
	Volume = {102},
	Year = {2007}
}

@misc{Davison2012b,
	Author = {Davison, A. C. and Gholamrezaee, M. M.},
	Booktitle = {Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Proceedings of the Royal Society A Mathematical, Physical and Engineering Sciences/Davison, Gholamrezaee - 2012 - Geostatistics of extremes.pdf:pdf},
	Issn = {1364-5021},
	Keywords = {composite marginal likelihood,extremal coefficient,max-stable process,spatial,statistics,statistics of extremes,temperature data},
	Number = {2138},
	Pages = {581--608},
	Title = {{Geostatistics of extremes}},
	Volume = {468},
	Year = {2012}
}

@article{Davison2013,
	Author = {Davison, A. C. and Huser, R. and Thibaud, E.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Mathematical Geosciences/Davison, Huser, Thibaud - 2013 - Geostatistics of Dependent and Asymptotically Independent Extremes.pdf:pdf},
	Issn = {1874-8961},
	Journal = {Mathematical Geosciences},
	Keywords = {asymptotic independence,brown,gaussian process,generalised pareto distribution,max-stable process,resnick process,statistics of extremes},
	Month = {jun},
	Number = {5},
	Pages = {511--529},
	Title = {{Geostatistics of Dependent and Asymptotically Independent Extremes}},
	Volume = {45},
	Year = {2013}
}

@article{Davison2012,
	Author = {Davison, A. C. and Padoan, S. A. and Ribatet, M.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Statistical Science/Davison, Padoan, Ribatet - 2012 - Statistical Modeling of Spatial Extremes.pdf:pdf},
	Issn = {0883-4237},
	Journal = {Statistical Science},
	Keywords = {Bayesian hierarchical model,Brown-Resnick process,and phrases,annual maximum analysis,bayesian hierarchical,brown,composite likelihood,copula,environmen-,environmental data analysis,gaussian process,generalized extreme-value distribution,geostatistics,latent variable,max-stable process,model,resnick process,statistics of extremes,tal data analysis},
	Month = {may},
	Number = {2},
	Pages = {161--186},
	Title = {{Statistical modeling of spatial extremes}},
	Volume = {27},
	Year = {2012}
}

@article{Davison2012a,
	Author = {Davison, A. C. and Padoan, S. A. and Ribatet, M.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Statistical Science/Davison, Padoan, Ribatet - 2012 - Rejoinder.pdf:pdf},
	Issn = {0883-4237},
	Journal = {Statistical Science},
	Month = {may},
	Number = {2},
	Pages = {199--201},
	Title = {{Rejoinder}},
	Volume = {27},
	Year = {2012}
}

@article{Davison1990,
	Abstract = {We discuss the analysis of the extremes of data by modelling the sizes and occurrence of ; ; exceedances over high thresholds. The natural distribution for such exceedances, the ; ; generalized Pareto distribution, is described and its properties elucidated. Estimation and ; ; model-checking procedures for univariate and regression data are developed, and the ; ; influence of and information contained in the most extreme observations in a sample are ; ; studied. Models for seasonality and serial dependence in the point process of exceedances ; ; are described. Sets of data on river flows and wave heights are discussed, and an application ; ; to the siting of nuclear installations is described.},
	Author = {Davison, Anthony C. and Smith, R. L.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of the Royal Statistical Society. Series B (Methodological)/Davison, Smith - 1990 - Models for exceedances over high thresholds (with Discussion).pdf:pdf},
	Isbn = {00359246},
	Issn = {00359246},
	Journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
	Keywords = {Generalized extreme value distribution,generalized pareto distribution,maximum likelihood,nuclear siting policy,peaks over threshold,point,process,profile likelihood,regression,return level,river flows,seasonality,statistics of extremes,wave heights},
	Number = {3},
	Pages = {393--442},
	Title = {{Models for exceedances over high thresholds (with Discussion)}},
	Volume = {52},
	Year = {1990}
}

@article{DeCarvalho2014,
	Author = {de Carvalho, Miguel and Davison, Anthony C.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of the American Statistical Association/de Carvalho, Davison - 2014 - Spectral Density Ratio Models for Multivariate Extremes.pdf:pdf},
	Issn = {0162-1459},
	Journal = {Journal of the American Statistical Association},
	Keywords = {air temperature,empirical likelihood,exponential tilting,forest microclimate,mul-,semiparametric modeling,spectral distribution,tivariate extreme values},
	Number = {506},
	Pages = {140106135135004},
	Title = {{Spectral Density Ratio Models for Multivariate Extremes}},
	Volume = {109},
	Year = {2014}
}

@book{deHaan2006,
	Author = {de Haan, L and Ferreira, A},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	Isbn = {9780387239460},
	Publisher = {Springer},
	Series = {Springer Series in Operations Research and Financial Engineering},
	Title = {{Extreme Value Theory: An Introduction}},
	Year = {2006}
}

@article{Demarta2005,
	Abstract = {The t copula and its properties are described with a focus on issues related to the dependence of extreme values. The Gaussian mixture representation of a multivariate t distribution is used as a starting point to construct two new copulas, the skewed t copula and the grouped t copula, which allow more heterogeneity in the modelling of dependent observations. Extreme value considerations are used to derive two further new copulas: the t extreme value copula is the limiting copula of componentwise maxima of t distributed random vectors; the t lower tail copula is the limiting copula of bivariate observations from a t distribution that are conditioned to lie below some joint threshold that is progressively lowered. Both these copulas may be approximated for practical purposes by simpler, better-known copulas, these being the Gumbel and Clayton copulas respectively.},
	Author = {Demarta, Stefano and McNeil, Alexander J.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/International Statistical Review/Demarta, McNeil - 2005 - The t Copula and Related Copulas.pdf:pdf},
	Issn = {03067734},
	Journal = {International Statistical Review},
	Keywords = {clayton copula,copula,extreme value theory,gumbel copula,kendall,multivariate,multivariate t distribution,s rank correlation,tail dependence},
	Month = {jan},
	Number = {1},
	Pages = {111--129},
	Title = {{The t Copula and Related Copulas}},
	Volume = {73},
	Year = {2005}
}

@article{DuMouchel1983,
	Author = {DuMouchel, William H.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/The Annals of Statistics/DuMouchel - 1983 - Estimating the stable index $\alpha$ in order to measure tail thickness a critique.pdf:pdf},
	Journal = {The Annals of Statistics},
	Number = {4},
	Pages = {1019--1031},
	Title = {{Estimating the stable index $\alpha$ in order to measure tail thickness: a critique}},
	Volume = {11},
	Year = {1983}
}

@article{Eckert2011,
	Author = {Eckert, Nicolas and Gaume, Johan and Castebrunet, Hel{\`{e}}ne},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Procedia Environmental Sciences/Eckert, Gaume, Castebrunet - 2011 - Using spatial and spatial-extreme statistics to characterize snow avalanche cycles(2).pdf:pdf},
	Issn = {18780296},
	Journal = {Procedia Environmental Sciences},
	Month = {jan},
	Pages = {224--229},
	Title = {{Using spatial and spatial-extreme statistics to characterize snow avalanche cycles}},
	Volume = {7},
	Year = {2011}
}

@article{Engelke2015,
	Author = {Engelke, Sebastian and Malinowski, Alexander and Kabluchko, Zakhar and Schlather, Martin},
	Date-Modified = {2016-06-27 19:38:39 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of the Royal Statistical Society. Series B Statistical Methodology/Engelke et al. - 2014 - Estimation of H{\"{u}}sler-Reiss distributions and Brown-Resnick processes.pdf:pdf},
	Issn = {13697412},
	Journal = {Journal of the Royal Statistical Society. Series B: Statistical Methodology},
	Keywords = {Extreme value theory,Max-stable process,Peaks-over-threshold method,Poisson point process,Spectral density},
	Volume = {77},
	Pages = {239--265},
	Title = {{Estimation of H{\"{u}}sler-Reiss distributions and Brown-Resnick processes}},
	Year = {2015},
	archivePrefix = {arXiv},
	eprint = {1207.6886v2}
}

@article{Engelke2014a,
	Author = {Engelke, Sebastian and Malinowski, Alexander and Oesting, Marco and Schlather, Martin},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Advances in Applied Probability/Engelke et al. - 2014 - Statistical inference for max-stable processes by conditioning on extreme events.pdf:pdf},
	Issn = {0001-8678},
	Journal = {Advances in Applied Probability},
	Keywords = {2010 mathematics subject classification,62e20,extreme value statistics,incremental representation,max-stable process,mixed moving maxima,peaks-over-threshold,primary 60g70,secondary 62g32},
	Number = {2},
	Pages = {478--495},
	Title = {{Statistical inference for max-stable processes by conditioning on extreme events}},
	Volume = {46},
	Year = {2014}
}

@article{EugeniaCastellanos2007,
	Author = {{Eugenia Castellanos}, M. and Cabras, Stefano},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of Statistical Planning and Inference/Eugenia Castellanos, Cabras - 2007 - A default Bayesian procedure for the generalized Pareto distribution.pdf:pdf},
	Issn = {03783758},
	Journal = {Journal of Statistical Planning and Inference},
	Keywords = {bayesian inference,extreme value theory,jeffreys,peaks over the threshold,s prior},
	Month = {feb},
	Number = {2},
	Pages = {473--483},
	Title = {{A default Bayesian procedure for the generalized Pareto distribution}},
	Volume = {137},
	Year = {2007}
}

@book{Falk2011,
	Address = {Basel},
	Author = {Falk, Michael and H{\"{u}}sler, J{\"{u}}rg and Reiss, Rolf-Dieter},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Unknown/Falk, H{\"{u}}sler, Reiss - 2011 - Laws of Small Numbers Extremes and Rare Events.pdf:pdf},
	Isbn = {978-3-0348-0008-2},
	Publisher = {Springer Basel},
	Title = {{Laws of Small Numbers: Extremes and Rare Events}},
	Year = {2011}
}

@article{Fuentes2012,
	Abstract = {Estimating the probability of extreme temperature events is difficult because of limited records across time and the need to extrapolate the distributions of these events, as opposed to just the mean, to locations where observations are not available. Another related issue is the need to characterize the uncertainty in the estimated probability of extreme events at different locations. Although the tools for statistical modeling of univariate extremes are well-developed, extending these tools to model spatial extreme data is an active area of research. In this paper, in order to make inference about spatial extreme events, we introduce a new nonparametric model for extremes. We present a Dirichlet-based copula model that is a flexible alternative to parametric copula models such as the normal and t-copula. The proposed modelling approach is fitted using a Bayesian framework that allow us to take into account different sources of uncertainty in the data and models. We apply our methods to annual maximum temperature values in the east-south-central United States.},
	Author = {Fuentes, Montserrat and Henry, John and Reich, Brian},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Extremes/Fuentes, Henry, Reich - 2012 - Nonparametric spatial models for extremes application to extreme temperature data.pdf:pdf},
	Issn = {1386-1999},
	Journal = {Extremes},
	Keywords = {dirichlet processes,extreme temperatures,nonstationarity,return levels,spatial models},
	Month = {aug},
	Number = {1},
	Pages = {75--101},
	Title = {{Nonparametric spatial models for extremes: application to extreme temperature data}},
	Volume = {16},
	Year = {2012}
}

@article{Gabda2012,
	Author = {Gabda, Darmesah and Towe, Ross and Wadsworth, Jennifer and Tawn, Jonathan},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Statistical Science/Gabda et al. - 2012 - Discussion of ``Statistical Modeling of Spatial Extremes'' by A. C. Davison, S. A. Padoan and M. Ribatet.pdf:pdf},
	Issn = {0883-4237},
	Journal = {Statistical Science},
	Month = {may},
	Number = {2},
	Pages = {189--192},
	Title = {{Discussion of ``Statistical Modeling of Spatial Extremes'' by A. C. Davison, S. A. Padoan and M. Ribatet}},
	Volume = {27},
	Year = {2012}
}

@article{Gelfand1998,
	Author = {Gelfand, Alan E. and Ghosh, Sujit K.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Biometrika/Gelfand, Ghosh - 1998 - Model choice a minimum posterior predictive loss approach.pdf:pdf},
	Issn = {0006-3444},
	Journal = {Biometrika},
	Month = {mar},
	Number = {1},
	Pages = {1--11},
	Title = {{Model choice: a minimum posterior predictive loss approach}},
	Volume = {85},
	Year = {1998}
}

@article{Gelfand2005,
	Abstract = {Customary modeling for continuous point-referenced data assumes a Gaussian process that is often taken to be stationary. When such models are fitted within a Bayesian framework, the unknown parameters of the process are assumed to be random, so a random Gaussian process results. Here we propose a novel spatial Dirichlet process mixture model to produce a random spatial process that is neither Gaussian nor stationary. We first develop a spatial Dirichlet process model for spatial data and discuss its properties. Because of familiar limitations associated with direct use of Dirichlet process models, we introduce mixing by convolving this process with a pure error process. We then examine properties of models created through such Dirichlet process mixing. In the Bayesian framework, we implement posterior inference using Gibbs sampling. Spatial prediction raises interesting questions, but these can be handled. Finally, we illustrate the approach using simulated data, as well as a dataset involving precipitation measurements over the Languedoc-Roussillon region in southern France.},
	Author = {Gelfand, Alan E. and Kottas, Athanasios and MacEachern, Steven N.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of the American Statistical Association/Gelfand, Kottas, MacEachern - 2005 - Bayesian Nonparametric Spatial Modeling With Dirichlet Process Mixing.pdf:pdf},
	Issn = {0162-1459},
	Journal = {Journal of the American Statistical Association},
	Keywords = {dependent dirichlet process,dirichlet process mixture models,gaussian process,markov chain monte carlo,nonsta-,point-referenced spatial data,random distribution,tionarity},
	Month = {sep},
	Number = {471},
	Pages = {1021--1035},
	Title = {{Bayesian Nonparametric Spatial Modeling With Dirichlet Process Mixing}},
	Volume = {100},
	Year = {2005}
}

@book{Genton2004,
	Author = {Genton, Marc G},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	Isbn = {9781584884316},
	Publisher = {Taylor {\&} Francis},
	Series = {Statistics (Chapman {\&} Hall/CRC)},
	Title = {{Skew-Elliptical Distributions and Their Applications: A Journey Beyond Normality}},
	Year = {2004}
}

@article{Gnedenko1943,
	Author = {Gnedenko, B.},
	File = {:Users/samorris/Dropbox/Mendeley/Annals of Mathematics/Gnedenko - 1943 - Sur la distribution limite du terme maximum d'une s{\'{e}}rie al{\'{e}}atoire.pdf:pdf},
	Journal = {Annals of Mathematics},
	Number = {3},
	Pages = {423----453},
	Title = {{Sur la distribution limite du terme maximum d'une s{\'{e}}rie al{\'{e}}atoire}},
	Volume = {44},
	Year = {1943}}

@article{Gneiting2006,
	Author = {Gneiting, Tilmann and Larson, Kristin and Westrick, Kenneth and Genton, Marc G and Aldrich, Eric},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of the American Statistical Association/Gneiting et al. - 2006 - Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center.pdf:pdf},
	Issn = {0162-1459},
	Journal = {Journal of the American Statistical Association},
	Month = {sep},
	Number = {475},
	Pages = {968--979},
	Title = {{Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center}},
	Volume = {101},
	Year = {2006}
}

@article{Gneiting2007,
	Abstract = {Scoring rules assess the quality of probabilistic forecasts, by assigning a numerical score based on the predictive distribution and on the event or value that materializes. A scoring rule is proper if the forecaster maximizes the expected score for an observation drawn from the distributionF if he or she issues the probabilistic forecast F, rather than G ≠ F. It is strictly proper if the maximum is unique. In prediction problems, proper scoring rules encourage the forecaster to make careful assessments and to be honest. In estimation problems, strictly proper scoring rules provide attractive loss and utility functions that can be tailored to the problem at hand. This article reviews and develops the theory of proper scoring rules on general probability spaces, and proposes and discusses examples thereof. Proper scoring rules derive from convex functions and relate to information measures, entropy functions, and Bregman divergences. In the case of categorical variables, we prove a rigorous version of the Savage representation. Examples of scoring rules for probabilistic forecasts in the form of predictive densities include the logarithmic, spherical, pseudospherical, and quadratic scores. The continuous ranked probability score applies to probabilistic forecasts that take the form of predictive cumulative distribution functions. It generalizes the absolute error and forms a special case of a new and very general type of score, the energy score. Like many other scoring rules, the energy score admits a kernel representation in terms of negative definite functions, with links to inequalities of Hoeffding type, in both univariate and multivariate settings. Proper scoring rules for quantile and interval forecasts are also discussed. We relate proper scoring rules to Bayes factors and to cross-validation, and propose a novel form of cross-validation known as random-fold cross-validation. A case study on probabilistic weather forecasts in the North American Pacific Northwest illustrates the importance of propriety. We note optimum score approaches to point and quantile estimation, and propose the intuitively appealing interval score as a utility function in interval estimation that addresses width as well as coverage.},
	Author = {Gneiting, Tilmann and Raftery, Adrian E.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of the American Statistical Association/Gneiting, Raftery - 2007 - Strictly Proper Scoring Rules, Prediction, and Estimation.pdf:pdf},
	Issn = {0162-1459},
	Journal = {Journal of the American Statistical Association},
	Keywords = {bayes factor,bregman divergence,brier score,coherent,continuous ranked probability score,cross-validation,distribution,entropy,kernel score,loss function,minimum contrast estimation,negative definite function,prediction interval,predictive,quantile forecast,scoring rule,skill score,strictly proper,utility function},
	Month = {mar},
	Number = {477},
	Pages = {359--378},
	Title = {{Strictly proper scoring rules, prediction, and estimation}},
	Volume = {102},
	Year = {2007}
}

@article{Gneiting2005,
	Author = {Gneiting, Tilmann and Raftery, Adrian E. and Westveld, Anton H. and Goldman, Tom},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Monthly Weather Review/Gneiting et al. - 2005 - Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation.pdf:pdf},
	Issn = {0027-0644},
	Journal = {Monthly Weather Review},
	Month = {may},
	Number = {5},
	Pages = {1098--1118},
	Title = {{Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation}},
	Volume = {133},
	Year = {2005}
}

@article{Gneiting2011,
	Author = {Gneiting, Tilmann and Ranjan, Roopesh},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of Business {\&} Economic Statistics/Gneiting, Ranjan - 2011 - Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules.pdf:pdf},
	Issn = {0735-0015},
	Journal = {Journal of Business {\&} Economic Statistics},
	Keywords = {Continuous ranked probability score, Predictive ab,continuous ranked probability score,predictive ability testing,probabilistic forecast,proper scoring rule,quantile,weighted likelihood ratio test},
	Month = {jul},
	Number = {3},
	Pages = {411--422},
	Title = {{Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules}},
	Volume = {29},
	Year = {2011}
}

@article{Grimit2006,
	Author = {Grimit, E. P. and Gneiting, T. and Berrocal, V. J. and Johnson, N. a.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Quarterly Journal of the Royal Meteorological Society/Grimit et al. - 2006 - The continuous ranked probability score for circular variables and its application to mesoscale forecast ensemble.pdf:pdf},
	Issn = {00359009},
	Journal = {Quarterly Journal of the Royal Meteorological Society},
	Month = {oct},
	Number = {621C},
	Pages = {2925--2942},
	Title = {{The continuous ranked probability score for circular variables and its application to mesoscale forecast ensemble verification}},
	Volume = {132},
	Year = {2006}
}

@article{Gupta2004a,
	Author = {Gupta, Arjun K. and Chen, John T.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Annals of the Institute of Statistical Mathematics/Gupta, Chen - 2004 - A class of multivariate skew-normal models.pdf:pdf},
	Issn = {0020-3157},
	Journal = {Annals of the Institute of Statistical Mathematics},
	Number = {2},
	Pages = {305--315},
	Title = {{A class of multivariate skew-normal models}},
	Volume = {56},
	Year = {2004}
}

@article{Gupta2004,
	Author = {Gupta, Arjun K. and Gonz{\'{a}}lez-Farı́as, Graciela and Domı́nguez-Molina, J.Armando},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of Multivariate Analysis/Gupta, Gonz{\'{a}}lez-Farı́as, Domı́nguez-Molina - 2004 - A multivariate skew normal distribution.pdf:pdf},
	Issn = {0047259X},
	Journal = {Journal of Multivariate Analysis},
	Keywords = {conditional,contours,density,generating function,marginal,moment,moments,non-normal models,regression},
	Month = {apr},
	Number = {1},
	Pages = {181--190},
	Title = {{A multivariate skew normal distribution}},
	Volume = {89},
	Year = {2004}
}

@book{Gupta2013,
	Address = {New York, NY},
	Author = {Gupta, Arjun K. and Varga, Tamas and Bodnar, Taras},
	Booktitle = {Elliptically Contoured Models in Statistics and Portfolio Theory},
	Chapter = {11},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Elliptically Contoured Models in Statistics and Portfolio Theory/Gupta, Varga, Bodnar - 2013 - Elliptically Contoured Models in Statistics and Portfolio Theory.pdf:pdf},
	Isbn = {978-1-4614-8153-9},
	Publisher = {Springer New York},
	Title = {{Elliptically Contoured Models in Statistics and Portfolio Theory}},
	Year = {2013}
}

@article{Han,
	Abstract = {We propose a robust alternative to principal component analysis (PCA) -- named elliptical component analysis (ECA) -- for analyzing high dimensional elliptically distributed data. ECA aims at estimating the eigenspace of the covariance matrix of the elliptical data. To cope with the heavy-tailed elliptical distributions, a multivariate rank statistic is exploited. At the model-level, we consider two settings that the leading eigenvectors of the covariance matrix are non-sparse or sparse. Methodologically, we propose ECA procedures corresponding to both non-sparse and sparse settings. Theoretically, we provide both non-asymptotic and asymptotic analysis in quantifying the theoretical performances of ECA. Under the non-sparse setting, we show that ECA's performance is highly related to the effective rank of the covariance matrix. Under the sparse setting, the results are in two folds: (i) We show that the sparse ECA estimator based on a combinatoric program attains the optimal rate of convergence; (ii) Built upon some recent developments in estimating sparse leading eigenvectors, we show that a computationally efficient sparse ECA estimator attains the optimal rate of convergence under a suboptimal scaling.},
	Author = {Han, Fang and Liu, Han},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/ArXiv e-prints/Han, Liu - 2013 - ECA High Dimensional Elliptical Component Analysis in non-Gaussian Distributions.pdf:pdf},
	Journal = {ArXiv e-prints},
	Keywords = {Elliptical component analysis,Elliptical distribution,Keyword,Multivariate Kendall's tau,Optimality property,Robust estimators,Sparse principal component analysis,to read},
	Mendeley-Tags = {to read},
	Month = {oct},
	Title = {{ECA: High Dimensional Elliptical Component Analysis in non-Gaussian Distributions}},
	Year = {2013},
	archivePrefix = {arXiv},
	eprint = {1310.3561}}

@article{Hersbach2000,
	Author = {Hersbach, Hans},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Weather and Forecasting/Hersbach - 2000 - Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems.pdf:pdf},
	Issn = {0882-8156},
	Journal = {Weather and Forecasting},
	Month = {oct},
	Number = {5},
	Pages = {559--570},
	Title = {{Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems}},
	Volume = {15},
	Year = {2000}
}

@phdthesis{Huser2013,
	Abstract = {Risk assessment for extreme natural phenomena has become increasingly important, and over the past few years the scientific community has realized the importance of considering the spatial or spatio-temporal extent of extreme events. Historically, the GEV and GPD distributions have played an important role in the statistical modeling of extremes at individual locations, but for risk assessment it is crucial to assess dependence between locations: if dependence is strong, extreme events might occur simultaneously at different locations, thereby increasing the overall risk. In this thesis, we construct new dependence models for space-time extremes, based on asymptotically justified arguments, and propose novel inference methods for fitting these models to observations exceeding high thresholds. So far, the modeling of spatial extremes has been limited to fitting max-stable processes to block (usually annual) maxima, regarded as mutually independent. Our threshold-based approach is more efficient and enables more detailed analysis of extremes, but requires a more sophisticated treatment of dependence. The present work also describes how composite likelihoods can be used for inference, establishes the asymptotic distribution of the corresponding estimators, and assesses statistical efficiency for these methods in various contexts. The methodology is illustrated by application to hourly rainfall data from western Switzerland, and enables realistic modeling of their extremal properties.},
	Author = {Huser, R.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Unknown/Huser - 2013 - Statistical Modeling and Inference for Spatio-Temporal Extremes.pdf:pdf},
	Keywords = {Asymptotic independence,Composite likelihood,Extreme event,Max- stable process,Rainfall data,Relative efficiency,Spatio-temporal dependence,Threshold exceedance},
	Pages = {291},
	Title = {{Statistical Modeling and Inference for Spatio-Temporal Extremes}},
	Volume = {5946},
	Year = {2013}
}

@article{Huser2014,
	Abstract = {Max-stable processes are the natural analogues of the generalized extreme-value distribution for the modelling of extreme events in space and time. Under suitable conditions, these processes are asymptotically justified models for maxima of independent replications of random fields, and they are also suitable for the modelling of joint individual extreme measurements over high thresholds. This paper extends a model of Schlather (2001) to the space-time framework, and shows how a pairwise censored likelihood can be used for consistent estimation under mild mixing conditions. Estimator efficiency is also assessed and the choice of pairs to be included in the pairwise likelihood is discussed based on computations for simple time series models. The ideas are illustrated by an application to hourly precipitation data over Switzerland.},
	Author = {Huser, R. and Davison, A. C.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of the Royal Statistical Society Series B (Statistical Methodology)/Huser, Davison - 2014 - Space-time modelling of extreme events.pdf:pdf},
	Issn = {13697412},
	Journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
	Keywords = {composite likelihood,dom set,extremal coefficient,max-stable process,rainfall data,ran-,threshold-based inference},
	Month = {mar},
	Number = {2},
	Pages = {439--461},
	Title = {{Space-time modelling of extreme events}},
	Volume = {76},
	Year = {2014},
	archivePrefix = {arXiv},
	eprint = {1201.3245}
}

@article{Husler1989,
	Abstract = {A new approach to the asymptotic treatment of multivariate sample maxima is suggested and exemplified in the particular case of maxima of normal random vectors. In the limit one obtains a class of multivariate maxstable distributions not considered in literature so far.},
	Author = {H{\"{u}}sler, J{\"{u}}rg and Reiss, Rolf-Dieter},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Statistics {\&} Probability Letters/H{\"{u}}sler, Reiss - 1989 - Maxima of normal random vectors Between independence and complete dependence.pdf:pdf},
	Issn = {01677152},
	Journal = {Statistics {\&} Probability Letters},
	Keywords = {correlation coefflcmnt,max-stability,muluvanate sample maxama,normal random vectors},
	Number = {4},
	Pages = {283 ---- 286},
	Title = {{Maxima of normal random vectors: Between independence and complete dependence}},
	Volume = {7},
	Year = {1989}
}

@phdthesis{Jeon2012a,
	Abstract = {The analysis of spatial extremes requires the joint modeling of a spatial process at a large number of stations. Multivariate extreme value theory can be used to model the joint extremal behavior of environmental data such as precipitation, snow depths or daily temperatures. Max-stable processes are the natural generalization of extremal dependence structures to infinite dimensions arising from the extension of multivariate extreme value theory. However, there have been few works on the threshold approach of max-stable processes. Padoan, Ribatet and Sisson [2010] proposed the maximum composite likelihood approach for fitting max-stable processes to avoid the complexity and unavailability of the multivariate density function. We propose the threshold version of max-stable process estimation and we apply the pairwise composite likelihood method to it. We assume a strict form of condition, so called the second-order regular variation condition, for the distribution satisfying the domain of attraction. To obtain the limit behavior, we also consider the increasing domain structure with stochastic sampling design based on the setting and conditions in Lahiri [2003] and we then establish consistency and asymptotic normality of the estimator for dependence parameter in the threshold method of max-stable processes. The method is studied by simulation and illustrated by the application of temperature data in North Carolina, United States.},
	Author = {Jeon, Soyoung},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Unknown/Jeon - 2012 - Max-stable Processes for Threshold Exceedances.pdf:pdf},
	Isbn = {9781267420602},
	School = {The University of North Carolina at Chapel Hill},
	Title = {{Max-stable Processes for Threshold Exceedances}},
	Year = {2012}
}

@article{Jeon2012,
	Abstract = {The analysis of spatial extremes requires the joint modeling of a spatial process at a large number of stations and max-stable processes have been developed as a class of stochastic processes suitable for studying spatial extremes. Spatial dependence structure in the extreme value analysis can be measured by max-stable processes. However, there have been few works on the threshold approach of max-stable processes. We propose a threshold version of max-stable process estimation and we apply the pairwise composite likelihood method by Padoan et al. (2010) to estimate spatial dependence parameters. It is of interest to establish limit behavior of the estimates based on the settings of increasing domain asymptotics with stochastic sampling design. Two different types of asymptotic normality are drawn under the second-order regular variation condition for the distribution satisfying the domain of attraction. The theoretical property of dependence parameter estimators in limiting sense is implemented by simulation and a choice of optimal threshold is discussed in this paper.},
	Author = {Jeon, Soyoung and Smith, Richard L.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Extremes/Jeon, Smith - 2012 - Dependence Structure of Spatial Extremes Using Threshold Approach.pdf:pdf},
	Journal = {Extremes},
	Month = {sep},
	Pages = {1--43},
	Title = {{Dependence Structure of Spatial Extremes Using Threshold Approach}},
	Year = {2012},
	archivePrefix = {arXiv},
	eprint = {1209.6344}
}

@article{Kanter1975,
	Abstract = {In this paper it is shown that if q is the density of a symmetric stable density, then for c∈(0,1)∪(1,∞), the graph of q(x) intersects the graph of cq(cx) at only two points. The argument proceeds by introducing a new characterization of unimodality for densities and involves a representation for symmetric stable random variables that is also useful for simulating such random variables. Finally our results are applied to prove some inequalities concerning the total variation norm of the difference of two symmetric stable densities.},
	Author = {Kanter, Marek},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/The Annals of Probability/Kanter - 1975 - Stable Densities Under Change of Scale and Total Variation Inequalities.pdf:pdf},
	Issn = {0091-1798},
	Journal = {The Annals of Probability},
	Keywords = {monotone likelihood ratio,stable density,total variation distance,totally positive kernel,unimodal density},
	Month = {aug},
	Number = {4},
	Pages = {697--707},
	Title = {{Stable Densities Under Change of Scale and Total Variation Inequalities}},
	Volume = {3},
	Year = {1975}
}

@article{Kim2005,
	Abstract = {In many problems in geostatistics the response variable of interest is strongly related to the underlying geology of the spatial location. In these situations there is often little correlation in the responses found in different rock strata, so the underlying covariance structure shows sharp changes at the boundaries of the rock types. Conventional stationary and nonstationary spatial methods are inappropriate, because they typically assume that the covariance between points is a smooth function of distance. In this article we propose a generic method for the analysis of spatial data with sharp changes in the underlying covariance structure. Our method works by automatically decomposing the spatial domain into disjoint regions within which the process is assumed to be stationary, but the data are assumed independent across regions. Uncertainty in the number of disjoint regions, their shapes, and the model within regions is dealt with in a fully Bayesian fashion. We illustrate our approach on a previously unpublished dataset relating to soil permeability of the Schneider Buda oil field in Wood County, Texas.},
	Author = {Kim, Hyoung-Moon and Mallick, Bani K and Holmes, C. C},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of the American Statistical Association/Kim, Mallick, Holmes - 2005 - Analyzing Nonstationary Spatial Data Using Piecewise Gaussian Processes.pdf:pdf},
	Issn = {0162-1459},
	Journal = {Journal of the American Statistical Association},
	Keywords = {bayes factor,carlo,kriging,model averaging,reversible-jump markov chain monte,voronoi tessellation},
	Number = {470},
	Pages = {653--668},
	Title = {{Analyzing nonstationary spatial data using piecewise Gaussian processes}},
	Volume = {100},
	Year = {2005}
}

@article{Kim2008,
	Abstract = {A critical issue in modelling binary response data is the choice of$\backslash$nthe links. We introduce a new link based on the generalized t-distribution.$\backslash$nThere are two parameters in the generalized t-link: one parameter$\backslash$npurely controls the heaviness of the tails of the link and the second$\backslash$nparameter controls the scale of the link. Two major advantages are$\backslash$noffered by the generalized t-links. First, a symmetric generalized$\backslash$nt-link with an unknown shape parameter is much more identifiable$\backslash$nthan a Student t-link with unknown degrees of freedom and a known$\backslash$nscale parameter. Secondly, skewed generalized t-links with both unknown$\backslash$nshape and scale parameters provide much more flexible and improved$\backslash$nskewed link regression models than the existing skewed links. Various$\backslash$ntheoretical properties and attractive features of the proposed links$\backslash$nare examined and explored in detail. An efficient Markov chain Monte$\backslash$nCarlo algorithm is developed for sampling from the posterior distribution.$\backslash$nThe deviance information criterion measure is used for guiding the$\backslash$nchoice of links. The proposed methodology is motivated and illustrated$\backslash$nby prostate cancer data.},
	Author = {Kim, Sungduk and Chen, Ming H. and Dey, Dipak K.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Biometrika/Kim, Chen, Dey - 2008 - Flexible generalized t-link models for binary response data.pdf:pdf},
	Issn = {00063444},
	Journal = {Biometrika},
	Keywords = {Latent variable; Logistic regression,Markov chain Monte Carlo,Mixed-effects model,Posterior distribution,Probit link,Robit link.},
	Number = {1},
	Pages = {93--106},
	Title = {{Flexible generalized t-link models for binary response data}},
	Volume = {95},
	Year = {2008}
}

@article{Kuk2000,
	Abstract = {The method of pairwise likelihood is investigated for analyzing clustered or longitudinal binary data. The pairwise likelihood is a product of bivariate likelihoods for within cluster pairs of observations, and its maximizer is the maximum pairwise likelihood estimator. We discuss the computational advantages of pairwise likelihood relative to competing approaches, present some efficiency calculations and argue that when cluster sizes are unequal a weighted pairwise likelihood should be used for the marginal regression parameters, whereas the unweighted pairwise likelihood should be used for the association parameters.},
	Author = {Kuk, Anthony Y.C. and Nott, David J.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Statistics {\&} Probability Letters/Kuk, Nott - 2000 - A pairwise likelihood approach to analyzing correlated binary data.pdf:pdf},
	Isbn = {0167-7152},
	Issn = {01677152},
	Journal = {Statistics {\&} Probability Letters},
	Keywords = {alternating logistic regression,composite likelihood,generalized estimating equations,marginal models,odds ratio,pairwise likelihood},
	Number = {4},
	Pages = {329--335},
	Title = {{A pairwise likelihood approach to analyzing correlated binary data}},
	Volume = {47},
	Year = {2000}
}

@article{Ledford1996,
	Abstract = {We propose a multivariate extreme value threshold model for joint tail estimation which overcomes the problems encountered with existing techniques when the variables are near independence. We examine inference under the model and develop tests for independence of extremes of the marginal variables, both when the thresholds are fixed, and when they increase with the sample size. Motivated by results obtained from this model, we give a new and widely applicable characterisation of dependence in the joint tail which includes existing models as special cases. A new parameter which governs the form of dependence is of fundamental importance to this characterisation. By estimating this parameter, we develop a diagnostic test which assesses the applicability of bivariate extreme value joint tail models. The methods are demonstrated through simulation and by analysing two previously published data sets.},
	Author = {Ledford, Anthony W. and Tawn, Jonathan A.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Biometrika/Ledford, Tawn - 1996 - Statistics for near independence in multivariate extreme values.pdf:pdf},
	Issn = {0006-3444},
	Journal = {Biometrika},
	Keywords = {QA Mathematics},
	Pages = {169--187},
	Title = {{Statistics for near independence in multivariate extreme values.}},
	Volume = {83},
	Year = {1996}
}

@article{Liseo2002,
	Author = {Liseo, Brunero and Loperfido, Nicola and La, Roma},
	File = {:Users/samorris/Dropbox/Mendeley/Unknown/Liseo, Loperfido, La - 2002 - Default Bayesian Analysis of the Skew-Normal Distribution.pdf:pdf},
	Title = {{Default Bayesian Analysis of the Skew-Normal Distribution}},
	Year = {2002}}

@article{Matheson1976,
	Author = {Matheson, JE and Winkler, RL},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Management Science/Matheson, Winkler - 1976 - Scoring Rules for Continuous Probability Distributions.pdf:pdf},
	Journal = {Management Science},
	Number = {10},
	Pages = {1087--1096},
	Title = {{Scoring Rules for Continuous Probability Distributions}},
	Volume = {22},
	Year = {1976}
}

@article{McMillan1992,
	Abstract = {Commonly-employed spatial autocorrelation models imply heterskedastic errors, but heteroskedasticity causes probit to be inconsistent. This paper proposes and illustrates the use of two categories of estimators for probit models with spatial autocorrelation. One category is based on the EM algorithm, and requires repeated application of a maximum-likelihood estimator. The other category, which can be applied to models derived using the spatial expansion method, only requires weighted least squares.},
	Author = {McMillen, Daniel P.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of Regional Science/McMillen - 1992 - Probit with spatial autocorrelation.pdf:pdf},
	Isbn = {1467-9787},
	Issn = {1467-9787},
	Journal = {Journal of Regional Science},
	Number = {3},
	Pages = {335--348},
	Title = {{Probit with spatial autocorrelation}},
	Volume = {32},
	Year = {1992}
}

@article{Michel2007,
	Abstract = {The investigation of multivariate generalized Pareto distributions (GPDs) has begun only recently. For further progress with these distributions simulation methods are an important part. We describe several methods of simulating GPDs, beginning with an efficient method for the logistic GPD. The algorithm is based on the Shi transformation, which was already used for the simulation of multivariate extreme value distributions (EVDs) of logistic type. In the sequel another algorithm is presented simulating a broader class of GPDs. Due to its numerical complexity it is only practicably applicable in low dimensions. A method is given to generate unconditional GPD random vectors from conditionally GPD distributed random vectors. A short application of the simulation methods in the analysis of a real hydrological data set concludes the article. The simulation algorithms are available on the author's home page http://statistik.mathematik.uni-wuerzburg.de/{\~{}}michel.},
	Author = {Michel, Ren{\'{e}}},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Extremes/Michel - 2007 - Simulation of certain multivariate generalized Pareto distributions.pdf:pdf},
	Isbn = {1068700700360},
	Issn = {1386-1999},
	Journal = {Extremes},
	Keywords = {60g70,62g32,ams 2000 subject classification,generalized pareto distribution,logistic gpd,peaks over threshold,pickands coordinates,primary,rejection method,secondary,shi transformation,simulation},
	Month = {oct},
	Number = {3},
	Pages = {83--107},
	Title = {{Simulation of certain multivariate generalized Pareto distributions}},
	Volume = {10},
	Year = {2007}
}

@article{Michel2008,
	Abstract = {The investigation of multivariate generalized Pareto distributions (GPDs) has begun only recently and there are slightly varying definitions of GPDs available. In this article we investigate the one from Section 5.1 of Falk et al. [Laws of Small Numbers: Extremes and Rare Events, second ed., Birkh{\"{a}}user, Basel, 2004], which does not differ in the area of interest from those of other authors. We first give an interpretation of the case of independence in terms of the peaks-over-threshold approach. This case is also used in dimension d=3 by Falk et al. [Laws of Small Numbers: Extremes and Rare Events, second ed., Birkh{\"{a}}user, Basel, 2004] as a counterexample to show that GP functions are not necessarily distribution functions on their entire support. We generalize this counterexample to an arbitrary dimension d≥3 and demonstrate also that other GP functions show this behavior. Finally we show that different GPDs can lead to the same conditional probability measure in the area of interest.},
	Author = {Michel, Ren{\'{e}}},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of Multivariate Analysis/Michel - 2008 - Some notes on multivariate generalized Pareto distributions.pdf:pdf},
	Issn = {0047259X},
	Journal = {Journal of Multivariate Analysis},
	Keywords = {angular measure,extreme value distribution,generalized pareto distribution,logistic,peaks over threshold},
	Month = {jul},
	Number = {6},
	Pages = {1288--1301},
	Title = {{Some notes on multivariate generalized Pareto distributions}},
	Volume = {99},
	Year = {2008}
}

@phdthesis{Michel2006,
	Abstract = {The investigation of multivariate generalized Pareto distributions (GPDs) in the framework of extreme value theory has begun only lately. Recent results show that they can, as in the univariate case, be used in Peaks over Threshold approaches. In this manuscript we investigate the definition of GPDs from Section 5.1 of Falk et al. (2004), which does not differ in the area of interest from those of other authors. We first show some theoretical properties and introduce important examples of GPDs. For the further investigation of these distributions simulation methods are an important part. We describe several methods of simulating GPDs, beginning with an efficient method for the logistic GPD. This algorithm is based on the Shi transformation, which was introduced by Shi (1995) and was used in Stephenson (2003) for the simulation of multivariate extreme value distributions of logistic type. We also present nonparametric and parametric estimation methods in GPD models. We estimate the angular density nonparametrically in arbitrary dimension, where the bivariate case turns out to be a special case. The asymptotic normality of the corresponding estimators is shown. Also in the parametric estimations, which are mainly based on maximum likelihood methods, the asymptotic normality of the estimators is shown under certain regularity conditions. Finally the methods are applied to a real hydrological data set containing water discharges of the rivers Altm{\"{u}}hl and Danube in southern Bavaria.},
	Author = {Michel, Ren{\'{e}}},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Unknown/Michel - 2006 - Simulation and estimation in multivariate generalized Pareto models.pdf:pdf},
	Keywords = {angular density,extreme value theory,multivariate generalized Pareto distributions,peaks over threshold,simulation},
	School = {Universit{\"{a}}t W{\"{u}}rtzburg},
	Title = {{Simulation and estimation in multivariate generalized Pareto models}},
	Year = {2006}
}

@article{Minozzo2012,
	Author = {Minozzo, Marco and Ferracuti, Laura},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Chilean Journal of Statistics (ChJS)/Minozzo, Ferracuti - 2012 - On the existence of some skew-normal stationary processes.pdf:pdf},
	Journal = {Chilean Journal of Statistics (ChJS)},
	Keywords = {autocorrelation function,generalized linear mixed model,geostatistics,mathematics subject classification,multivariate skew-normal distribution,primary 62m30,secondary 62h11,spatial process,stationary process},
	Number = {2},
	Pages = {157--170},
	Title = {{On the existence of some skew-normal stationary processes.}},
	Volume = {3},
	Year = {2012}
}

@article{Minozzo2004,
	Author = {Minozzo, Marco and Fruttini, Daniela},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Environmetrics/Minozzo, Fruttini - 2004 - Loglinear spatial factor analysis an application to diabetes mellitus complications.pdf:pdf},
	Issn = {1180-4009},
	Journal = {Environmetrics},
	Keywords = {disease mapping,generalized linear mixed models,linear model of coregionalization,loglinear,models,multivariate geostatistics,spatial epidemiology},
	Month = {aug},
	Number = {5},
	Pages = {423--434},
	Title = {{Loglinear spatial factor analysis: an application to diabetes mellitus complications}},
	Volume = {15},
	Year = {2004}
}

@article{Morrison1972,
	Abstract = {There are many types of diverse models that yield probabilistic predictions for outcomes that are binary. We show that a correlation between the 0, 1 binary outcomes and the prediction probabilities can yield meaningful results. The upper bound for the resulting R2 turns out to be a surprisingly simple expression for a class of situations which contain a quite general assumption on the distribution of the prediction probabilities. Some implications for subjective probability estimates are also given.},
	Author = {Morrison, Donald G.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of the American Statistical Association/Morrison - 1972 - Upper Bounds for Correlations between Binary Outcomes and Probabilistic Predictions.pdf:pdf},
	Issn = {0162-1459},
	Journal = {Journal of the American Statistical Association},
	Month = {mar},
	Number = {337},
	Pages = {68--70},
	Title = {{Upper Bounds for Correlations between Binary Outcomes and Probabilistic Predictions}},
	Volume = {67},
	Year = {1972}
}

@article{Nascimento2011,
	Author = {Nascimento, Fernando Ferraz and Gamerman, Dani and Lopes, Hedibert Freitas},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Statistics and Computing/Nascimento, Gamerman, Lopes - 2011 - A semiparametric Bayesian approach to extreme value estimation.pdf:pdf},
	Isbn = {1122201192},
	Issn = {0960-3174},
	Journal = {Statistics and Computing},
	Keywords = {bayesian,gpd,higher quantiles,mcmc,nonparametric estimation of curves,threshold estimation},
	Month = {aug},
	Number = {2},
	Pages = {661--675},
	Title = {{A semiparametric Bayesian approach to extreme value estimation}},
	Volume = {22},
	Year = {2011}
}

@book{Nelson2006,
	Address = {New York},
	Author = {Nelson, Roger B.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	Isbn = {978-0-387-28659-4},
	Publisher = {Springer-Verlag},
	Title = {{An Introduction to Copulas}},
	Year = {2006}
}

@article{OHagan1976,
	Abstract = {When estimating a normal location parameter, an inequality constraint is sometimes indicated by theoretical considerations. If, however, this constraint is assumed apriori definitely to hold, then the data will never be permitted to formally contradict the theory. We propose a family of skew prior distributions which reflect uncertainty about the constraint.},
	Author = {O'Hagan, A. and Leonard, Tom},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Biometrika/O'Hagan, Leonard - 1976 - Bayes Estimation Subject to Uncertainty About Parameter Constraints.pdf:pdf},
	Issn = {00063444},
	Journal = {Biometrika},
	Keywords = {1,background and motivation,flexible parametric family,graphical models,heavy tails,hidden truncation model,selective sampling,skew- t distribution,skew-elliptical distribution,skew-normal distribution,stochastic frontier models},
	Month = {apr},
	Number = {1},
	Pages = {201},
	Title = {{Bayes Estimation Subject to Uncertainty About Parameter Constraints}},
	Volume = {63},
	Year = {1976}
}

@article{Opitz2013,
	Abstract = {The extremal t process was proposed in the literature for modeling spatial extremes within a copula framework based on the extreme value limit of elliptical t distributions (Davison etal. (2012) [5]). A major drawback of this max-stable model was the lack of a spectral representation such that for instance direct simulation was infeasible. The main contribution of this note is to propose such a spectral construction for the extremal t process. Interestingly, the extremal Gaussian process introduced by Schlather (2002) [22] appears as a special case. We further highlight the role of the extremal t process as the maximum attractor for processes with finite-dimensional elliptical distributions. All results naturally also hold within the multivariate domain. {\textcopyright} 2013 Elsevier Inc.},
	Author = {Opitz, T.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of Multivariate Analysis/Opitz - 2013 - Extremal t processes Elliptical domain of attraction and a spectral representation.pdf:pdf},
	Issn = {0047259X},
	Journal = {Journal of Multivariate Analysis},
	Keywords = {Elliptical distribution,Extremal t process,Max-stable process,Spectral construction},
	Pages = {409--413},
	Publisher = {Elsevier Inc.},
	Title = {{Extremal t processes: Elliptical domain of attraction and a spectral representation}},
	Volume = {122},
	Year = {2013}
}

@article{Padoan2010,
	Author = {Padoan, S. A. and Ribatet, M. and Sisson, S. A.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of the American Statistical Association/Padoan, Ribatet, Sisson - 2010 - Likelihood-Based Inference for Max-Stable Processes(2).pdf:pdf},
	Issn = {0162-1459},
	Journal = {Journal of the American Statistical Association},
	Keywords = {Extreme value theory,Multivariate extreme analysis,Pseudo-likelihood,Rainfall,Spatial dependence,Spatial extremes,extreme value theory,multivariate extreme analysis,pseudo-likelihood,rainfall,spatial dependence,spatial extremes},
	Month = {mar},
	Number = {489},
	Pages = {263--277},
	Title = {{Likelihood-based inference for max-stable processes}},
	Volume = {105},
	Year = {2010}
}

@article{Padoan2011,
	Author = {Padoan, Simone A.},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/Journal of Multivariate Analysis/Padoan - 2011 - Multivariate extreme models based on underlying skew- and skew-normal distributions.pdf:pdf},
	Issn = {0047259X},
	Journal = {Journal of Multivariate Analysis},
	Month = {may},
	Number = {5},
	Pages = {977--991},
	Publisher = {Elsevier Inc.},
	Title = {{Multivariate extreme models based on underlying skew- and skew-normal distributions}},
	Volume = {102},
	Year = {2011}
}

@article{Reich2007,
	Abstract = {Storm surge, the onshore rush of sea water caused by the high winds and low pressure associated with a hurricane, can compound the effects of inland flooding caused by rainfall, leading to loss of property and loss of life for residents of coastal areas. Numerical ocean models are essential for creating storm surge forecasts for coastal areas. These models are driven primarily by the surface wind forcings. Currently, the gridded wind fields used by ocean models are specified by deterministic formulas that are based on the central pressure and location of the storm center. While these equations incorporate important physical knowledge about the structure of hurricane surface wind fields, they cannot always capture the asymmetric and dynamic nature of a hurricane. A new Bayesian multivariate spatial statistical modeling framework is introduced combining data with physical knowledge about the wind fields to improve the estimation of the wind vectors. Many spatial models assume the data follow a Gaussian distribution. However, this may be overly-restrictive for wind fields data which often display erratic behavior, such as sudden changes in time or space. In this paper we develop a semiparametric multivariate spatial model for these data. Our model builds on the stick-breaking prior, which is frequently used in Bayesian modeling to capture uncertainty in the parametric form of an outcome. The stick-breaking prior is extended to the spatial setting by assigning each location a different, unknown distribution, and smoothing the distributions in space with a series of kernel functions. This semiparametric spatial model is shown to improve prediction compared to usual Bayesian Kriging methods for the wind field of Hurricane Ivan.},
	Author = {Reich, Brian J. and Fuentes, Montserrat},
	Date-Modified = {2016-06-06 22:06:33 +0000},
	File = {:Users/samorris/Dropbox/Mendeley/The Annals of Applied Statistics/Reich, Fuentes - 2007 - A multivariate semiparametric Bayesian spatial modeling framework for hurricane surface wind fields.pdf:pdf},
	Issn = {1932-6157},
	Journal = {The Annals of Applied Statistics},
	Keywords = {Hierarchical Bayesian model,Multivariate data,Spatial statistics,Stick-breaking prior,Wind fields},
	Month = {jun},
	Number = {1},
	Pages = {249--264},
	Title = {{A multivariate semiparametric Bayesian spatial modeling framework for hurricane surface wind fields}},
	Volume = {1},
	Year = {2007}
}

@article{Reich2013,
	Author = {Reich, Brian J. and Porter, Michael D.},
	File = {:Users/samorris/Dropbox/Mendeley/The Annals of Applied Statistics/Reich, Porter - 2013 - Discussion of clauset and woodard.pdf:pdf},
	Journal = {The Annals of Applied Statistics},
	Pages = {1--5},
	Title = {{Discussion of clauset and woodard}},
	Year = {2013}}

@article{Reich2012,
	Abstract = {Extreme environmental phenomena such as major precipitation events manifestly exhibit spatial dependence. Max-stable processes are a class of asymptotically-justified models that are capable of representing spatial dependence among extreme values. While these models satisfy modeling requirements, they are limited in their utility because their corresponding joint likelihoods are unknown for more than a trivial number of spatial locations, preventing, in particular, Bayesian analyses. In this paper, we propose a new random effects model to account for spatial dependence. We show that our specification of the random effect distribution leads to a max-stable process that has the popular Gaussian extreme value process (GEVP) as a limiting case. The proposed model is used to analyze the yearly maximum precipitation from a regional climate model.},
	Author = {Reich, Brian J. and Shaby, Benjamin A.