Estimating the expectation of the log-likelihood with censored data for estimator selection

Benoit Liquet*, Daniel Commenges

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

A criterion for choosing an estimator in a family of semi-parametric estimators from incomplete data is proposed. This criterion is the expected observed log-likelihood (ELL). Adapted versions of this criterion in case of censored data and in presence of explanatory variables are exhibited. We show that likelihood cross-validation (LCV) is an estimator of ELL and we exhibit three bootstrap estimators. A simulation study considering both families of kernel and penalized likelihood estimators of the hazard function (indexed on a smoothing parameter) demonstrates good results of LCV and a bootstrap estimator called ELLbboot. We apply the ELLbboot criterion to compare the kernel and penalized likelihood estimators to estimate the risk of developing dementia for women using data from a large cohort study.

Original languageEnglish
Pages (from-to)351-367
Number of pages17
JournalLifetime Data Analysis
Volume10
Issue number4
DOIs
Publication statusPublished - Dec 2004
Externally publishedYes

Keywords

  • bootstrap
  • cross-validation
  • Kullback–Leibler information
  • semi-parametric
  • smoothing

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