Rates of convergence for multivariate normal approximation with applications to dense graphs and doubly indexed permutation statistics

被引：9
|
作者：
Fang, Xiao [1 ]
Roellin, Adrian [2 ]
机构：
[1] Stanford Univ, Dept Stat, Stanford, CA 94305 USA
[2] Natl Univ Singapore, Dept Stat & Appl Probabl, Singapore 117546, Singapore
来源：
BERNOULLI | 2015年 / 21卷 / 04期
关键词：
dense graph limits; multivariate normal approximation; non-smooth metrics; permutation statistics; random graphs; Stein's method; CENTRAL-LIMIT-THEOREM; STEINS METHOD; DEPENDENCE; SEQUENCES; CLT;
D O I：
10.3150/14-BEJ639
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
We provide a new general theorem for multivariate normal approximation on convex sets. The theorem is formulated in terms of a multivariate extension of Stein couplings. We apply the results to a homogeneity test in dense random graphs and to prove multivariate asymptotic normality for certain doubly indexed permutation statistics.
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页码：2157 / 2189
页数：33
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