In this paper, we discuss Bayesian inference of unobserved heterogeneity for unemployment duration data in the presence of right and interval-censoring, and non-proportionality. We employ accelerated failure time models with three different distributional assumptions: log-logistic, log-normal,...

Models for survival data generally assume that covariates are fully observed. However, in medical studies it is not uncommon for biomarkers to be censored at known detection limits. A computationally-efficient multiple imputation procedure for modeling survival data with covariates subject to...

As a generalization of the accelerated failure time models, we consider parametric models of lifetime Y, where the conditional mean E(Y|X;beta) can depend nonlinearly on the covariates X and some parameters beta. The error distribution can be heteroscedastic and dependent on X. With observed...

The frailty model is one of the most popular models used to analyze clustered failure time data, where the frailty term is used to assess an association within each cluster. The frailty model based on the semiparametric accelerated failure time model attracts less attention than the one based on...

The problem of estimating the parameters in a generalized linear model when a covariate is subject to censoring is studied. A new method based on an estimating function approach is proposed. The method does not assume a parametric form for the distribution of the response given the regressors...

Quantile regression offers a semiparametric approach to modeling data with possible heterogeneity. It is particularly attractive for censored responses, where the conditional mean functions are unidentifiable without parametric assumptions on the distributions. A new algorithm is proposed to...

Based on Stute’s weighted least squares method, we consider the estimate procedures for the accelerated failure time (AFT) model with high dimensional covariates. We use Kaplan–Meier weights and Stute’s estimator to account for censoring in least squares estimation. We consider two...