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

Benoit Liquet*, Jérôme Saracco, Daniel Commenges

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)619-634
Number of pages16
JournalComputational Statistics
Issue number4
Publication statusPublished - Dec 2007
Externally publishedYes


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


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