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CE_32T Wed, 8/6/2014, 10:00 AM - 11:45 AM CC-154
Model Selection Methods with Examples from SAS/STAT Software — Professional Development Computer Technology Workshop
ASA , SAS
When you are faced with a predictive modeling problem that has many possible predictor effects - dozens, hundreds or even thousands - a natural question is, "What subset of the effects provides the best model for the data?" This tutorial explains how you can address this question with modern model selection methods for linear and generalized linear models. The tutorial also explores the practical pitfalls of model selection, including issues which have led experts to criticize the validity of some methods. You will learn how to use the following approaches:  Model selection diagnostics, including graphics, for detecting problems.  Use of validation data to detect and prevent under-fitting and over-fitting.  Modern penalty-based methods, including LASSO, adaptive LASSO and elastic net as alternatives to traditional methods such as stepwise selection.  Bootstrap-based model averaging to reduce selection bias and improve predictive performance. This tutorial focuses on applications of the GLMSELECT procedure, but it also features the new HPGENSELECT procedure for model selection in generalized linear models and the HPREG procedures for variable screening.
Instructor(s): Funda Gunes, SAS Institute



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