Estimation in the Semiparametric Accelerated Failure Time Model With Missing Covariates: Improving Efficiency Through Augmentation

Semiparametric Maximum Likelihood Estimation in Parametric Regression with Missing Covariates

Asymptotic theory for the semiparametric accelerated failure time model with missing data

We consider a class of doubly weighted rank-based estimating methods for the transformation (or accelerated failure time) model with missing data as arise, for example, in case-cohort studies. The weights considered may not be predictable as required in a martingale stochastic process formulation. We treat the general problem as a semiparametric estimating equation problem and provide proofs of...

Adjusted regularized estimation in the accelerated failure time model with high dimensional covariates

We consider two regularization approaches, the LASSO and the threshold-gradient-directed regularization, for estimation and variable selection in the accelerated failure time model with multiple covariates based on Stute's weighted least squares method. The Stute estimator uses Kaplan-Meier weights to account for censoring in the least squares criterion. The weighted least squares objective fun...

Semiparametric estimation with generated covariates

Semiparametric Estimation with Generated Covariates In this paper, we study a general class of semiparametric optimization estimators of a vectorvalued parameter. The criterion function depends on two types of infinite-dimensional nuisance parameters: a conditional expectation function that has been estimated nonparametrically using generated covariates, and another estimated function that is u...

Bayesian Semiparametric Inference for the Accelerated Failure Time Model

Bayesian semi-parametric inference is considered for a log-linear model. This model consists of a parametric component for the regression coeecients and a nonparametric component for the unknown error distribution. Bayesian analysis is studied for the case of a parametric prior on the regression coeecients and a mixture-of-Dirichlet-processes prior on the unknown error distribution. A Markov ch...