# Multivariate Central Limit Theorem For Non-Identical Distribution [closed]

Among the different generalizations of the CLT available on the web, I found these

• CLT for the sum of non-identical (and independent) random variables
• CLT for the sum of identical (and independent) multivariate random variables.

However, I can't find any for the sum of non-identical (and independent) multivariate variables.

Is it because it is straightforward ? Would the final covariance matrix be simply the sum of the individual ones ?

Thanks

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## closed as off-topic by Did, Ryan Budney, Mark Meckes, Andrés E. Caicedo, Chris GodsilJul 15 '13 at 3:08

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I always prefer to have error bounds for the CLT, so my favorite reference for your question is the paper "A Lyapunov type bound in $\mathbb{R}^d$" by Vidmantas Bentkus (Theory of Probability & Its Applications 49(2), 311--323, 2005).
From the abstract: Let $X_1, \dots, X_n$ be independent, mean-zero, $\mathbb{R}^d$-valued random variables. Let $S = X_1 + \cdots + X_n$ and let $C^2$ be the covariance matrix of $S$, assumed invertible. Let $Z$ be a $d$-dimensional Gaussian with mean zero and covariance $C^2$. Then for any convex subset $A \subseteq \mathbb{R}^d$,
$$|\Pr[S \in A] - \Pr[Z \in A]| \leq O(d^{1/4}) \cdot \beta,$$ where $\beta = \sum_{i} \mathbf{E}[|C^{-1}X_i|^3]$. This is a $d$-dimensional generalization of the Berry--Esseen Theorem.