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Choosing a Model Selection Strategy

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  • XAVIER DE LUNA
  • KOSTAS SKOURAS

Abstract

An important problem in statistical practice is the selection of a suitable statistical model. Several model selection strategies are available in the literature, having different asymptotic and small sample properties, depending on the characteristics of the data generating mechanism. These characteristics are difficult to check in practice and there is a need for a data‐driven adaptive procedure to identify an appropriate model selection strategy for the data at hand. We call such an identification a model metaselection, and we base it on the analysis of recursive prediction residuals obtained from each strategy with increasing sample sizes. Graphical tools are proposed in order to study these recursive residuals. Their use is illustrated on real and simulated data sets. When necessary, an automatic metaselection can be performed by simply accumulating predictive losses. Asymptotic and small sample results are presented.

Suggested Citation

  • Xavier De Luna & Kostas Skouras, 2003. "Choosing a Model Selection Strategy," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 30(1), pages 113-128, March.
    • Handle: RePEc:bla:scjsta:v:30:y:2003:i:1:p:113-128
    DOI: 10.1111/1467-9469.00321
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    1. Clarke, Bertrand, 1999. "Combining model selection procedures for online prediction," Technical Reports 1999,46, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
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