Robust and Efficient Parameter Estimation based on Censored Data with Stochastic Covariates

TL;DR

This paper introduces a robust parametric estimator for censored lifetime data with stochastic covariates, using the minimum density power divergence approach, which maintains efficiency and robustness against outliers.

Contribution

It proposes a novel robust estimation method for censored data with stochastic covariates, improving robustness over traditional likelihood-based methods.

Findings

The estimator is robust to outliers in censored regression data.

Simulation studies show competitive efficiency with maximum likelihood estimators.

Theoretical asymptotic properties are established for the proposed estimator.

Abstract

Analysis of random censored life-time data along with some related stochastic covariables is of great importance in many applied sciences like medical research, population studies and planning etc. The parametric estimation technique commonly used under this set-up is based on the efficient but non-robust likelihood approach. In this paper, we propose a robust parametric estimator for the censored data with stochastic covariates based on the minimum density power divergence approach. The resulting estimator also has competitive efficiency with respect to the maximum likelihood estimator under pure data. The strong robustness property of the proposed estimator with respect to the presence of outliers is examined and illustrated through an appropriate simulation study in the context of censored regression with stochastic covariates. Further, the theoretical asymptotic properties of the proposed estimator are also derived in terms of a general class of M-estimators based on the estimating equation.