Weighted Breslow-type and maximum likelihood estimation in semiparametric transformation models

Maximum likelihood estimation in semiparametric regression models with censored data

Semiparametric regression models play a central role in formulating the effects of covariates on potentially censored failure times and in the joint modelling of incomplete repeated measures and failure times in longitudinal studies. The presence of infinite dimensional parameters poses considerable theoretical and computational challenges in the statistical analysis of such models. We present ...

Maximum penalized likelihood estimation in semiparametric capture-recapture models

We consider a semiparametric modeling approach for capture-recapture-recovery data where the temporal and/or individual variation of model parameters – usually the demographic parameters – is explained via covariates. Typically, in such analyses a fixed (or mixed) effects parametric model is specified for the relationship between the model parameters and the covariates of interest. In this pape...

Semiparametric Maximum Likelihood Estimation in Normal Transformation Models for Bivariate Survival Data.

We consider a class of semiparametric normal transformation models for right censored bivariate failure times. Nonparametric hazard rate models are transformed to a standard normal model and a joint normal distribution is assumed for the bivariate vector of transformed variates. A semiparametric maximum likelihood estimation procedure is developed for estimating the marginal survival distributi...

Maximum likelihood estimation for semiparametric transformation models with interval-censored data

Interval censoring arises frequently in clinical, epidemiological, financial and sociological studies, where the event or failure of interest is known only to occur within an interval induced by periodic monitoring. We formulate the effects of potentially time-dependent covariates on the interval-censored failure time through a broad class of semiparametric transformation models that encompasse...

Semiparametric Maximum Likelihood Estimation in Parametric Regression with Missing Covariates