Premium
Semiparametric smoothing of discrete failure time data
Author(s) -
Patil Prakash N.,
Bagkavos Dimitrios
Publication year - 2012
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201100058
Subject(s) - estimator , smoothing , nonparametric statistics , mathematics , parametric statistics , semiparametric model , hazard , covariate , markov chain , parametric model , statistics , econometrics , chemistry , organic chemistry
An estimator of the hazard rate function from discrete failure time data is obtained by semiparametric smoothing of the (nonsmooth) maximum likelihood estimator, which is achieved by repeated multiplication of a Markov chain transition‐type matrix. This matrix is constructed so as to have a given standard discrete parametric hazard rate model, termed the vehicle model, as its stationary hazard rate. As with the discrete density estimation case, the proposed estimator gives improved performance when the vehicle model is a good one and otherwise provides a nonparametric method comparable to the only purely nonparametric smoother discussed in the literature. The proposed semiparametric smoothing approach is then extended to hazard models with covariates and is illustrated by applications to simulated and real data sets.