Selection between proportional and stratified hazards models based on expected log-likelihood
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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.
KeywordsKullback–Leibler information Likelihood cross-validation Model selection Proportional hazards model Smoothing Stratified hazards model
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- Akaike H (1973) Information theory and an extension of the maximum likelihood principle. In: Petrov B, Csaki F (eds) Second International Symposium on Information Theory. Budapest, Akademiai Kiado, pp 267–281Google Scholar
- DeLeeuw J (1992) Introduction to Akaike (1973) information theory and an extension of the maximum likelihood principle. In: Kotz S, Johnson NL (eds) Breakthroughs in statistics, vol I. Foundations and basic theory. Springer, New York, pp 599–609Google Scholar