Measuring Inaccuracies in the Proportional Hazard Rate Model based on Extropy using a Length-Biased Weighted Residual approach

TL;DR

This paper introduces a new weighted residual inaccuracy measure based on extropy for proportional hazard rate models, providing theoretical properties, estimators, and practical applications to improve data analysis accuracy.

Contribution

It extends residual inaccuracy measures to a weighted version using extropy, with new properties, inequalities, estimators, and real data application.

Findings

Proposed a new weighted extropy-inaccuracy measure.

Derived properties and inequalities for the measure.

Demonstrated effectiveness through simulations and real data.

Abstract

In this paper, we consider the concept of the residual inaccuracy measure and extend it to its weighted version based on extropy. Properties of this measure are studied and the discrimination principle is applied in the class of proportional hazard rate (PHR) models. A characterization problem for the proposed weighted extropy-inaccuracy measure is studied. We propose some alternative expressions of weighted residual measure of inaccuracy. Additionally, we establish upper and lower limits and various inequalities related to the weighted residual inaccuracy measure using extropy. Non-parametric estimators based on the kernel density estimation method and empirical distribution function for the proposed measure are obtained and the performance of the estimators are also discussed using some simulation studies. Finally, a real dataset is applied for illustrating our new proposed measure. In general, our study highlights the potential of the weighted residual inaccuracy measure based on extropy as a powerful tool for improving the quality and reliability of data analysis and modelling across various disciplines. Researchers and practitioners can benefit from incorporating this measure into their analytical toolkit to enhance the accuracy and effectiveness of their work.