Further study on inferential aspects of log-Lindley distribution with an application of stress-strength reliability in insurance

TL;DR

This paper explores Bayesian parameter estimation for the log-Lindley distribution, compares it with classical methods, and applies stress-strength reliability analysis to insurance data, demonstrating Bayesian advantages through simulations and real-world application.

Contribution

It introduces Bayesian estimation for the log-Lindley distribution and develops stress-strength reliability inference under both classical and Bayesian frameworks.

Findings

Bayesian approach shows marked improvement over classical methods with reasonable priors.

Simulation studies validate the effectiveness of Bayesian estimation.

Application to insurance data demonstrates practical utility of stress-strength reliability metrics.

Abstract

The log-Lindley distribution was recently introduced in the literature as a viable alternative to the Beta distribution. This distribution has a simple structure and possesses useful theoretical properties relevant in insurance. Classical estimation methods have been well studied. We introduce estimation of parameters from Bayesian point of view for this distribution. Explicit structure of stress-strength reliability and its inference under both classical and Bayesian set-up is addressed. Extensive simulation studies show marked improvement with Bayesian approach over classical given reasonable prior information. An application of a useful metric of discrepancy derived from stress-strength reliability is considered and computed for two categories of firm with respect to a certain financial indicator.