Weighted Mathai-Haubold entropy measures improve reliability analysis
On Weighted Mathai-Haubold Entropy Measures
Information Theory
Summary
Measuring uncertainty in systems that change over time can help predict failure or aging. This paper introduces new versions of an entropy measure called weighted Mathai-Haubold entropy and shows how to apply it to study aging in systems. The authors also created mathematical tools to estimate these measures from data and tested their accuracy using computer simulations.
What this means in practice
- •For reliability engineers: Assess system aging characteristics using new weighted entropy measures to improve maintenance planning.
- •For data analysts in healthcare: Use weighted residual entropy to analyze patient survival data for better understanding of time-dependent risks.
Authors
Oindrali Das, Siddhartha Chakraborty
Abstract
In this paper, we propose weighted Mathai-Haubold entropy along with their residual and past versions and study their properties. We develop aging classes based on the weighted Mathai-Haubold residual and past entropy measures. Also, some inequalities related to the three proposed measures are discussed. Non-parametric estimators of the proposed measures are introduced and their properties are investigated. Performance of this estimator is evaluated by means of bias and mean squared error using Monte-Carlo simulations.