Statistical model choice

Gerda Claeskens

No 505677, Working Papers of Department of Decision Sciences and Information Management, Leuven from KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven

Abstract: Variable selection methods and model selection approaches are valuable statistical tools, which are indispensable for almost any statistical modeling question. This review considers first the use of information criteria for model selection. Such criteria provide an ordering of the considered models where the `best' model is selected. Different modeling goals, though, might require different criteria to be used. Next, the effect of including a penalty in the estimation process is discussed. Also nonparametric estimation contains several aspects of model choice, such as the choice of the estimator to use and the selection of tuning parameters. Fourth, model averaging approaches are reviewed where estimators from different models are weighted to provide one final estimator. There are several ways to choose the weights, most of them result in data-driven, hence random, weights. Challenges for inference after model selection and inference for model averaged estimators are discussed.

Keywords: Model selection; Variable selection; Information criteria; Model averaging (search for similar items in EconPapers)
Date: 2015
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Published in FEB Research Report KBI_1521

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