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Title:A Bayesian solution to non-convergence of crossed random effects models
Author(s):Huang, Mingya
Advisor(s):Anderson, Carolyn Jane
Contributor(s):Kern, Justin; Zhang, Jinming
Department / Program:Educational Psychology
Discipline:Educational Psychology
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Crossed-Random Effects Models, convergence, MLE, REML, Bayesian model estimation
Abstract:When observations (trials) are nested within combinations of subjects and stimuli, crossed random effects models simultaneously take into account both fixed effects and random effects of the subjects and stimuli; however, maximum likelihood estimation (MLE) and restricted maximum likelihood (REML) estimation often encounter convergence problems, which in turn lead to researchers fitting simpler models (e.g., only random intercepts). If the random effect structure is too simple, tests of fixed effects are not valid; if the random effect structure is too complex, tests of fixed effects are inefficient. This study examines issues of estimation, convergence and problems inherent with MLE and REML. We investigated whether Bayesian estimation can solve the convergence problem through a simulation study, which makes a case for adopting a Bayesian approach for estimation, especially when using crossed random effects models. In our simulation study were found that both MLE and REML encountered convergence problems even when trying to fit the correctly specified model and simpler versions of it. Bayesian estimation of models converged 100% of the time, and for the correctly specified model, the parameter estimates were accurate estimates for both fixed and random effects and were essentially unbiased. In sum, the Bayesian approach is a viable alternative to MLE/REML, because models fit by Bayesian estimation solves the non-convergence problem and yields valid and efficient estimates.
Issue Date:2019-07-22
Rights Information:Copyright 2019 Mingya Huang
Date Available in IDEALS:2020-03-02
Date Deposited:2019-12

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