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 language | English |
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Pages (from-to) | 351-367 |
Number of pages | 17 |
Journal | Lifetime Data Analysis |
Volume | 10 |
Issue number | 4 |
DOIs | |
Publication status | Published - Dec 2004 |
Externally published | Yes |
Keywords
- bootstrap
- cross-validation
- Kullback–Leibler information
- semi-parametric
- smoothing