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http://dx.doi.org/10.29220/CSAM.2020.27.3.301

Bayesian information criterion accounting for the number of covariance parameters in mixed effects models  

Heo, Junoh (Department of Statistics, Chung-Ang University)
Lee, Jung Yeon (Department of Psychiatry, New York University School of Medicine)
Kim, Wonkuk (Department of Applied Statistics, Chung-Ang University)
Publication Information
Communications for Statistical Applications and Methods / v.27, no.3, 2020 , pp. 301-311 More about this Journal
Abstract
Schwarz's Bayesian information criterion (BIC) is one of the most popular criteria for model selection, that was derived under the assumption of independent and identical distribution. For correlated data in longitudinal studies, Jones (Statistics in Medicine, 30, 3050-3056, 2011) modified the BIC to select the best linear mixed effects model based on the effective sample size where the number of parameters in covariance structure was not considered. In this paper, we propose an extended Jones' modified BIC by considering covariance parameters. We conducted simulation studies under a variety of parameter configurations for linear mixed effects models. Our simulation study indicates that our proposed BIC performs better in model selection than Schwarz's BIC and Jones' modified BIC do in most scenarios. We also illustrate an example of smoking data using a longitudinal cohort of cancer patients.
Keywords
correlated data; effective sample size; Fisher information matrix; longitudinal study; model selection;
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