Bernoulli

  • Bernoulli
  • Volume 21, Number 4 (2015), 2157-2189.

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

Xiao Fang and Adrian Röllin

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Abstract

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.

Article information

Source
Bernoulli, Volume 21, Number 4 (2015), 2157-2189.

Dates
Received: June 2012
Revised: April 2014
First available in Project Euclid: 5 August 2015

Permanent link to this document
https://projecteuclid.org/euclid.bj/1438777590

Digital Object Identifier
doi:10.3150/14-BEJ639

Mathematical Reviews number (MathSciNet)
MR3378463

Zentralblatt MATH identifier
1344.60024

Keywords
dense graph limits multivariate normal approximation non-smooth metrics permutation statistics random graphs Stein’s method

Citation

Fang, Xiao; Röllin, Adrian. Rates of convergence for multivariate normal approximation with applications to dense graphs and doubly indexed permutation statistics. Bernoulli 21 (2015), no. 4, 2157--2189. doi:10.3150/14-BEJ639. https://projecteuclid.org/euclid.bj/1438777590


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