Progressive First-Failure Censoring in Reliability Analysis: Inference for a New Weibull–Pareto Distribution

Rashad M. EL-Sagheer () and Mahmoud M. Ramadan
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Rashad M. EL-Sagheer: High Institute of Computers and Management Information Systems, First Statement, New Cairo 11865, Cairo, Egypt
Mahmoud M. Ramadan: Department of Mathematics, Faculty of Education, Ain Shams University, Roxy 11341, Cairo, Egypt

Mathematics, 2025, vol. 13, issue 15, 1-28

Abstract: This paper explores statistical techniques for estimating unknown lifetime parameters using data from a progressive first-failure censoring scheme. The failure times are modeled with a new Weibull–Pareto distribution. Maximum likelihood estimators are derived for the model parameters, as well as for the survival and hazard rate functions, although these estimators do not have explicit closed-form solutions. The Newton–Raphson algorithm is employed for the numerical computation of these estimates. Confidence intervals for the parameters are approximated based on the asymptotic normality of the maximum likelihood estimators. The Fisher information matrix is calculated using the missing information principle, and the delta technique is applied to approximate confidence intervals for the survival and hazard rate functions. Bayesian estimators are developed under squared error, linear exponential, and general entropy loss functions, assuming independent gamma priors. Markov chain Monte Carlo sampling is used to obtain Bayesian point estimates and the highest posterior density credible intervals for the parameters and reliability measures. Finally, the proposed methods are demonstrated through the analysis of a real dataset.

Keywords: new Weibull–Pareto distribution; Bayesian estimation; maximum likelihood estimation; progressive first-failure censoring; reliability analysis; Markov chain Monte Carlo (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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