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

Liquet, Benoit and Commenges, Daniel (2004) Estimating the expectation of the log-likelihood with censored data for estimator selection. Lifetime Data Analysis, 10 4: 351-367. doi:10.1007/s10985-004-4772-z


Author Liquet, Benoit
Commenges, Daniel
Title Estimating the expectation of the log-likelihood with censored data for estimator selection
Journal name Lifetime Data Analysis   Check publisher's open access policy
ISSN 1380-7870
1572-9249
Publication date 2004-12
Year available 2004
Sub-type Article (original research)
DOI 10.1007/s10985-004-4772-z
Volume 10
Issue 4
Start page 351
End page 367
Total pages 17
Place of publication New York, NY United States
Publisher Springer New York LLC
Collection year 2005
Language eng
Formatted 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
Keyword Bootstrap
Cross validation
Kullback Leibler information
Semi parametric
Smoothing
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Mathematics and Physics
 
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Created: Tue, 17 Sep 2013, 15:25:19 EST by Kay Mackie on behalf of School of Mathematics & Physics