An Alternate Approach to Pseudo-Likelihood Model Selection in the Generalized Linear Mixed Modeling Framework

Patrick Ten Eyck () and Joseph E. Cavanaugh
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Patrick Ten Eyck: The University of Iowa
Joseph E. Cavanaugh: The University of Iowa

Sankhya B: The Indian Journal of Statistics, 2018, vol. 80, issue 1, No 6, 98-122

Abstract: Abstract In this paper, we propose and investigate an alternate approach to pseudo-likelihood model selection in the generalized linear mixed modeling framework. The problem with the natural approach to the computation of pseudo-likelihood model selection criteria is that the pseudo-data vary for each candidate model, leading to criteria based on fundamentally different goodness-of-fit statistics, rendering them incomparable. We propose a technique that circumvents this problem. This new approach can be implemented using a SAS macro that obtains and applies the pseudo-data from the full model to fitting candidate models based on all possible subsets of predictor variables. We justify the propriety of the resulting pseudo-likelihood selection criteria through an extensive study designed as a factorial experiment. We then illustrate this new method in a modeling application pertaining to bullying in public schools. The data set for the application is taken from three waves of the Iowa Youth Survey.

Keywords: Akaike information criterion; Generalized linear mixed model; Pseudo-likelihood; Variable selection; Primary 62J12; Secondary 62F07 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s13571-017-0130-5

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