Choosing a Model Selection Strategy

Choosing a Model Selection Strategy 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Scandinavian Journal of Statistics Wiley

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Publisher
Wiley
Copyright
Copyright © 2003 Wiley Subscription Services, Inc., A Wiley Company
ISSN
0303-6898
eISSN
1467-9469
DOI
10.1111/1467-9469.00321
Publisher site
See Article on Publisher Site

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.

Journal

Scandinavian Journal of StatisticsWiley

Published: Mar 1, 2003

References

  • An improvement of Akaike's FPE criterion to reduce its variability
    De Luna, De Luna
  • MDL denoising
    Rissanen, Rissanen
  • On efficient point prediction systems
    Skouras, Skouras; Dawid, Dawid

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