Dual divergences estimation for censored survival data

TL;DR

This paper introduces dual divergences estimators for robust parameter estimation in right censored survival data, providing theoretical limit laws and demonstrating their effectiveness through simulations.

Contribution

It proposes a novel dual divergences approach for robust estimation in censored survival models, with established asymptotic properties.

Findings

Estimators are consistent and asymptotically normal.

Simulation results confirm robustness and efficiency.

Method outperforms traditional estimators in censored data scenarios.

Abstract

This paper is devoted to robust estimation based on dual divergences estimators for parametric models in the framework of right censored data. We give limit laws of the proposed estimators and examine their asymptotic properties through a simulation study.