Maximum augmented empirical likelihood estimation of categorical marginal models for large sparse contingency tables

van der Ark, L. Andries, Bergsma, Wicher P. ORCID: 0000-0002-2422-2359 and Koopman, Letty (2023) Maximum augmented empirical likelihood estimation of categorical marginal models for large sparse contingency tables. Psychometrika, 88 (4). 1228 - 1248. ISSN 0033-3123

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Abstract

Categorical marginal models (CMMs) are flexible tools for modelling dependent or clustered categorical data, when the dependencies themselves are not of interest. A major limitation of maximum likelihood (ML) estimation of CMMs is that the size of the contingency table increases exponentially with the number of variables, so even for a moderate number of variables, say between 10 and 20, ML estimation can become computationally infeasible. An alternative method, which retains the optimal asymptotic efficiency of ML, is maximum empirical likelihood (MEL) estimation. However, we show that MEL tends to break down for large, sparse contingency tables. As a solution, we propose a new method, which we call maximum augmented empirical likelihood (MAEL) estimation and which involves augmentation of the empirical likelihood support with a number of well-chosen cells. Simulation results show good finite sample performance for very large contingency tables.

Item Type:	Article
Additional Information:	© 2023, The Author(s).
Divisions:	Statistics
Subjects:	B Philosophy. Psychology. Religion > BF Psychology Q Science > QA Mathematics
Date Deposited:	10 Oct 2023 15:03
Last Modified:	18 Nov 2024 19:42
URI:	http://eprints.lse.ac.uk/id/eprint/120434

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