Author Information

Matthew Kramer

Abstract

Stepwise model selection is a commonly used technique in regression when there are many candidate independent variables and limited time to develop a model. This approach was adapted to the mixed models framework and gives good results, established by simulation with a known model and by application to real world data. Model selection is done using an information criterion (selected by the user). The application is primarily written in Perl. The Perl code tracks which variables are in or out of the model, calculates the information criterion, and writes and submits SAS code. Proc Mixed in SAS is used to compute the log-likelihood for a model, which is used to calculate the information criterion, which then is used to judge whether the model has improved by adding or dropping a variable, or by changing the covariance structure of the residuals. The software is currently restricted to the case where the random part of the model is assumed to be known, but how to augment the software to also select the structure for the random part of the model is discussed.

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Apr 25th, 12:00 PM

AUTOMATIC MODEL SELECTION IN THE MIXED MODELS FRAMEWORK

Stepwise model selection is a commonly used technique in regression when there are many candidate independent variables and limited time to develop a model. This approach was adapted to the mixed models framework and gives good results, established by simulation with a known model and by application to real world data. Model selection is done using an information criterion (selected by the user). The application is primarily written in Perl. The Perl code tracks which variables are in or out of the model, calculates the information criterion, and writes and submits SAS code. Proc Mixed in SAS is used to compute the log-likelihood for a model, which is used to calculate the information criterion, which then is used to judge whether the model has improved by adding or dropping a variable, or by changing the covariance structure of the residuals. The software is currently restricted to the case where the random part of the model is assumed to be known, but how to augment the software to also select the structure for the random part of the model is discussed.