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February 2018 Inference in Ising models
Bhaswar B. Bhattacharya, Sumit Mukherjee
Bernoulli 24(1): 493-525 (February 2018). DOI: 10.3150/16-BEJ886

Abstract

The Ising spin glass is a one-parameter exponential family model for binary data with quadratic sufficient statistic. In this paper, we show that given a single realization from this model, the maximum pseudolikelihood estimate (MPLE) of the natural parameter is $\sqrt{a_{N}}$-consistent at a point whenever the log-partition function has order $a_{N}$ in a neighborhood of that point. This gives consistency rates of the MPLE for ferromagnetic Ising models on general weighted graphs in all regimes, extending the results of Chatterjee (Ann. Statist. 35 (2007) 1931–1946) where only $\sqrt{N}$-consistency of the MPLE was shown. It is also shown that consistent testing, and hence estimation, is impossible in the high temperature phase in ferromagnetic Ising models on a converging sequence of simple graphs, which include the Curie–Weiss model. In this regime, the sufficient statistic is distributed as a weighted sum of independent $\chi^{2}_{1}$ random variables, and the asymptotic power of the most powerful test is determined. We also illustrate applications of our results on synthetic and real-world network data.

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Bhaswar B. Bhattacharya. Sumit Mukherjee. "Inference in Ising models." Bernoulli 24 (1) 493 - 525, February 2018. https://doi.org/10.3150/16-BEJ886

Information

Received: 1 November 2015; Revised: 1 July 2016; Published: February 2018
First available in Project Euclid: 27 July 2017

zbMATH: 06778338
MathSciNet: MR3706767
Digital Object Identifier: 10.3150/16-BEJ886

Keywords: exponential family , graph limit theory , Hypothesis testing , Ising model , pseudolikelihood estimation , Spin glass

Rights: Copyright © 2018 Bernoulli Society for Mathematical Statistics and Probability

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Vol.24 • No. 1 • February 2018
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