Parameter estimation based on cumulative Kullback-Leibler divergence

TL;DR

This paper introduces new parameter estimators using cumulative Kullback-Leibler divergence for continuous survival models, with proven asymptotic properties and statistical inference methods.

Contribution

It develops a novel class of estimators based on Kullback-Leibler divergence, extending generalized estimating equations for survival analysis.

Findings

Estimators are consistent and asymptotically normal.

Asymptotic confidence intervals and hypothesis tests are derived.

The methods are theoretically validated for statistical inference.

Abstract

In this paper, we propose some estimators for the parameters of a statistical model based on Kullback-Leibler divergence of the survival function in continuous setting. We prove that the proposed estimators are subclass of "generalized estimating equations" estimators. The asymptotic properties of the estimators such as consistency, asymptotic normality, asymptotic confidence interval and asymptotic hypothesis testing are investigated.