The inverse Lindley distribution: A stress-strength reliability model

TL;DR

This paper introduces the inverse Lindley distribution, explores its properties, and demonstrates its application as a stress-strength reliability model for survival data, with Bayesian and classical estimation methods validated through simulations and real data.

Contribution

It proposes a new inverse Lindley distribution, analyzes its properties, and applies it as a novel stress-strength reliability model with comprehensive Bayesian and classical estimation techniques.

Findings

Distribution is heavy-tailed with upside-down bathtub failure rate.

Bayesian and classical estimators are compared via simulation.

Real data analysis demonstrates the model's applicability.

Abstract

In this article, we proposed an inverse Lindley distribution and studied its fundamental properties such as quantiles, mode, stochastic ordering and entropy measure. The proposed distribution is observed to be a heavy-tailed distribution and has a upside-down bathtub shape for its failure rate. Further, we proposed its applicability as a stress-strength reliability model for survival data analysis. The estimation of stress-strength parameters and $R=P[X>Y]$, the stress-strength reliability has been approached by both classical and Bayesian paradigms. Under Bayesian set-up, non-informative (Jeffrey) and informative (gamma) priors are considered under a symmetric (squared error) and a asymmetric (entropy) loss functions, and a Lindley-approximation technique is used for Bayesian computation. The proposed estimators are compared in terms of their mean squared errors through a simulation study. Two real data sets representing survival of Head and Neck cancer patients are fitted using the inverse Lindley distribution and used to estimate the stress-strength parameters and reliability.