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Computational Statistics

, Volume 22, Issue 4, pp 619–634 | Cite as

Selection between proportional and stratified hazards models based on expected log-likelihood

  • Benoit LiquetEmail author
  • Jérôme Saracco
  • Daniel Commenges
Original Paper

Abstract

The problem of selecting between semi-parametric and proportional hazards models is considered. We propose to make this choice based on the expectation of the log-likelihood (ELL) which can be estimated by the likelihood cross-validation (LCV) criterion. The criterion is used to choose an estimator in families of semi-parametric estimators defined by the penalized likelihood. A simulation study shows that the ELL criterion performs nearly as well in this problem as the optimal Kullback–Leibler criterion in term of Kullback–Leibler distance and that LCV performs reasonably well. The approach is applied to a model of age-specific risk of dementia as a function of sex and educational level from the data of a large cohort study.

Keywords

Kullback–Leibler information Likelihood cross-validation Model selection Proportional hazards model Smoothing Stratified hazards model 

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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Benoit Liquet
    • 1
    Email author
  • Jérôme Saracco
    • 2
  • Daniel Commenges
    • 3
  1. 1.INSERM U875, ISPEDUniversité Bordeaux 2Bordeaux CedexFrance
  2. 2.GREThA, UMR CNRS 5113Université Montesquieu-Bordeaux IVPessac CedexFrance
  3. 3.INSERM U875, ISPEDUniversité Bordeaux 2Bordeaux cedexFrance

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