Robust model selection in generalized linear models

Samuel Müller*, A. H. Welsh

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

In this paper, we extend to generalized linear models the robust model selection methodology of Müller and Welsh (2005 As in Müller and Welsh (2005), we combine a robust penalized measure of fit to the sample with a robust measure of out of sample predictive ability that is estimated using a post-stratified m-out-of-n bootstrap. The method can be used to compare different estimators (robust and nonrobust) as well as different models. Specialized to linear models, the present methodology improves on Müller and Welsh (2005): we use a new bias-adjusted bootstrap estimator which avoids the need to include an intercept in every model and we establish an essential monotonicity condition more generally.

Original languageEnglish
Pages (from-to)1155-1170
Number of pages16
JournalStatistica Sinica
Volume19
Issue number3
Publication statusPublished - Jul 2009
Externally publishedYes

Keywords

  • Bootstrap model selection
  • generalized linear models
  • paired bootstrap
  • robust estimation
  • robust model selection
  • stratified bootstrap

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