arXiv:1111.3159 [math.PR]AbstractReferencesReviewsResources
On the error bound in a combinatorial central limit theorem
Published 2011-11-14, updated 2015-04-13Version 5
Let $\mathbb{X}=\{X_{ij}: 1\le i,j\le n\}$ be an $n\times n$ array of independent random variables where $n\ge2$. Let $\pi$ be a uniform random permutation of $\{1,2,\dots,n\}$, independent of $\mathbb{X}$, and let $W=\sum_{i=1}^nX_{i\pi(i)}$. Suppose $\mathbb{X}$ is standardized so that ${\mathbb{E}}W=0,\operatorname {Var}(W)=1$. We prove that the Kolmogorov distance between the distribution of $W$ and the standard normal distribution is bounded by $451\sum_{i,j=1}^n{\mathbb{E}}|X_{ij}|^3/n$. Our approach is by Stein's method of exchangeable pairs and the use of a concentration inequality.
Comments: Published at http://dx.doi.org/10.3150/13-BEJ569 in the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)
Journal: Bernoulli 2015, Vol. 21, No. 1, 335-359
DOI: 10.3150/13-BEJ569
Keywords: combinatorial central limit theorem, error bound, standard normal distribution, uniform random permutation, independent random variables
Tags: journal article
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