Multiway empirical likelihood

TL;DR

This paper introduces a new multiway empirical likelihood method for statistical inference on data indexed by multiple entities, with improved properties and broad applications.

Contribution

It develops a novel multiway empirical likelihood statistic that converges to a chi-square distribution and extends to degenerate cases with higher-order accuracy.

Findings

Converges to chi-square under non-degenerate cases

Modified version works for degenerate cases with better higher-order properties

Applicable to bipartite networks, GEE, and three-way data

Abstract

This paper develops a general methodology to conduct statistical inference for observations indexed by multiple sets of entities. We propose a novel multiway empirical likelihood statistic that converges to a chi-square distribution under the non-degenerate case, where corresponding Hoeffding type decomposition is dominated by linear terms. Our methodology is related to the notion of jackknife empirical likelihood but the leave-out pseudo values are constructed by leaving columns or rows. We further develop a modified version of our multiway empirical likelihood statistic, which converges to a chi-square distribution regardless of the degeneracy, and discover its desirable higher-order property compared to the t-ratio by the conventional Eicker-White type variance estimator. The proposed methodology is illustrated by several important statistical problems, such as bipartite network, generalized estimating equations, and three-way observations.