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Non‐parametric Maximum‐Likelihood Estimation in a Semiparametric Mixture Model for Competing‐Risks Data
Author(s) -
CHANG ISHOU,
HSIUNG CHAO A.,
WEN CHICHUNG,
WU YUHJENN,
YANG CHECHI
Publication year - 2007
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/j.1467-9469.2007.00567.x
Subject(s) - identifiability , mathematics , semiparametric model , restricted maximum likelihood , covariate , parametric statistics , estimator , econometrics , statistics , marginal likelihood , semiparametric regression , parametric model , maximum likelihood
. This paper describes our studies on non‐parametric maximum‐likelihood estimators in a semiparametric mixture model for competing‐risks data, in which proportional hazards models are specified for failure time models conditional on cause and a multinomial model is specified for the marginal distribution of cause conditional on covariates. We provide a verifiable identifiability condition and, based on it, establish an asymptotic profile likelihood theory for this model. We also provide efficient algorithms for the computation of the non‐parametric maximum‐likelihood estimate and its asymptotic variance. The success of this method is demonstrated in simulation studies and in the analysis of Taiwan severe acute respiratory syndrome data.