TITLE: Distance between distributions and distance of moments
QUESTION [9 upvotes]: Let's say I have a sequence of random variables $X_n$ such that $$\mathbf E X_n^k = \mathbf E X^k+O(a_k/\sqrt{n})\quad\text{for all }k\in\mathbb N,\tag{$\ast$}$$ where $X$ is a random variable of standard (zero mean, unit variance) Gaussian distribution and $a_k$ are some constants which typically grow with $k$ (for my purposes they would be $a_k=(k/2)!$, for example). 
Since the normal distribution is uniquely determined by its moments $(\ast)$ implies that $X_n\Rightarrow X$ weakly, as $n\to\infty$. 
Is there an appropriate way to formalize the distance $d$ between the distributions of $X_n$ and $X$, such that $(\ast)$ implies $$d(X_n,X)=O(f(n))$$ for some function $f$? I think $(\ast)$ is not strong enough to control distance measures like the Kullback-Leibler divergence or the Hellinger distance, but there might be some appropriate weaker notion?
I think that $(\ast)$ implies $$\mathbf E f(X_n)=\mathbf E f(X)+O(1/\sqrt{n})$$ for a small class of test functions $f$, but depending on the growth of $a_k$, this class might be vary small.

REPLY [4 votes]: The classical approach to this is called "smoothing" and is described in Chapter 16 of Feller's Introduction to Probability Theory, Vol 2. Basically you use the bounds on the moment differences to bound the difference between the characteristic functions near the origin, then adjust a parameter (called $T$ by Feller) to get the best result.