On the order statistics from the XLindley distribution and associated inference with an application to fatigue data

TL;DR

This paper derives explicit formulas for order statistics from the XLindley distribution, enabling improved parameter estimation, prediction, and inference, with applications demonstrated through simulations and real data analysis.

Contribution

It introduces explicit formulas for moments of order statistics from the XLindley distribution and applies these to parameter estimation, prediction, and inference methods.

Findings

Explicit formulas for moments of order statistics derived.

Effective estimators for location and scale parameters identified.

Simulation and real data analysis validate the methods.

Abstract

In this paper, we consider the order statistics from a newly-introduced lifetime distribution called the XLindley distribution. We have derived explicit closed form expressions for the single moments and product moments of order statistics from the XLindley distribution. Utilizing these expressions, we calculated the means, variances, and covariances of order statistics for sample sizes ranging from n = 1 to n = 10 and arbitrarily selected parameter values. Additionally, these moments allow us to identify the best linear unbiased estimators and best linear invariant estimators for the location and scale parameters based on both complete samples and Type-II right censored samples. We also address the linear prediction of unobserved order statistics based on Type-II right-censored samples. We also explore the formulation of confidence intervals for location and scale parameters, along with prediction intervals for unobserved order statistics. To provide comparison and illustration, we conduct a simulation study and analyze a real data example. Finally, we conclude with several remarks.