Electronic Journal of Statistics

Berry-Esseen bounds for estimating undirected graphs

Larry Wasserman, Mladen Kolar, and Alessandro Rinaldo

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We consider the problem of providing nonparametric confidence guarantees — with finite sample Berry-Esseen bounds — for undirected graphs under weak assumptions. We do not assume sparsity or incoherence. We allow the dimension $D$ to increase with the sample size $n$. First, we prove lower bounds that show that if we want accurate inferences with weak assumptions then $D$ must be less than $n$. In that case, we show that methods based on Normal approximations and on the bootstrap lead to valid inferences and we provide new Berry-Esseen bounds on the accuracy of the Normal approximation and the bootstrap. When the dimension is large relative to sample size, accurate inferences for graphs under weak assumptions are not possible. Instead we propose to estimate something less demanding than the entire partial correlation graph. In particular, we consider: cluster graphs, restricted partial correlation graphs and correlation graphs.

Article information

Electron. J. Statist., Volume 8, Number 1 (2014), 1188-1224.

First available in Project Euclid: 12 August 2014

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62H12: Estimation
Secondary: 62H10: Distribution of statistics

Graphical models high dimensional inference


Wasserman, Larry; Kolar, Mladen; Rinaldo, Alessandro. Berry-Esseen bounds for estimating undirected graphs. Electron. J. Statist. 8 (2014), no. 1, 1188--1224. doi:10.1214/14-EJS928. https://projecteuclid.org/euclid.ejs/1407848859

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