|Title||Longitudinal Variable Selection by Cross-Validation in the Case of Many Covariates|
|Publication Type||Journal Article|
|Year of Publication||2007|
|Authors||Cantoni, E, Field, C, J. Fleming, M, Ronchetti, E|
|Journal||Statistics in Medicine|
Longitudinal models are commonly used for studying data collected on individuals repeatedly through time. While there are now a variety of such models available (marginal models, mixed eects models, etc.), far fewer options exist for the closely related issue of variable selection. In addition, longitudinal data typically derive from medical or other large-scale studies where often large numbers of potential explanatory variables and hence even larger numbers of candidate models must be considered. Crossvalidation is a popular method for variable selection based on the predictive ability of the model. Here, we propose a cross-validation Markov chain Monte Carlo procedure as a general variable selection tool which avoids the need to visit all candidate models. Inclusion of a one-standard error rule provides users with a collection of good models as is often desired. We demonstrate the eectiveness of our procedure both in a simulation setting and in a real application.
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