Journal article
An Alternate Approach to Pseudo-Likelihood Model Selection in the Generalized Linear Mixed Modeling Framework
Sankhya B, Vol.80(1), pp.98-122
05/2018
DOI: 10.1007/s13571-017-0130-5
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.
Details
- Title: Subtitle
- An Alternate Approach to Pseudo-Likelihood Model Selection in the Generalized Linear Mixed Modeling Framework
- Creators
- Patrick Ten Eyck - 0000 0004 1936 8294 grid.214572.7 Institute for Clinical and Translational Science The University of Iowa Iowa City IA USAJoseph Cavanaugh - 0000 0004 1936 8294 grid.214572.7 Department of Biostatistics The University of Iowa Iowa City IA USA
- Resource Type
- Journal article
- Publication Details
- Sankhya B, Vol.80(1), pp.98-122
- DOI
- 10.1007/s13571-017-0130-5
- ISSN
- 0976-8386
- eISSN
- 0976-8394
- Publisher
- Springer India; New Delhi
- Language
- English
- Date published
- 05/2018
- Academic Unit
- Statistics and Actuarial Science; Biostatistics; Injury Prevention Research Center
- Record Identifier
- 9983985800402771
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