Work

Bayesian Analysis of the Maxwell Distribution Under Progressively Type-II Random Censoring

Year: 2025

Type: article

Abstract: Accurate modeling of product lifetimes is vital in reliability analysis and engineering to ensure quality and maintain competitiveness. This paper proposes the progressively randomly censored Maxwell distribution, which incorporates both progressive Type-II and random censoring within the Maxwell distribution framework. The model allows for the planned removal of surviving units at specific stages of an experiment, accounting for both deliberate and random censoring events. It is assumed that survival and censoring times each follow a Maxwell distribution, though with distinct parameters. Both frequentist and Bayesian approaches are employed to estimate the model parameters. In the frequentist approach, maximum likelihood estimators and their corresponding confidence intervals are derived. In the Bayesian approach, Bayes estimators are obtained using an inverse gamma prior and evaluated through a Markov Chain Monte Carlo (MCMC) method under the squared error loss function (SELF). A Monte Carlo simulation study evaluates the performance of the proposed estimators. The practical relevance of the methodology is demonstrated using a real data set.

Source: Axioms

DOI: 10.3390/axioms14080573

Authors: Rajni Goel, Mahmoud M. Abdelwahab, Mustafa M. Hasaballah

Institutions: Dehradun Institute of Technology University, Imam Mohammad ibn Saud Islamic University

Language: English

Topic: Statistical Distribution Estimation and Applications

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: Deanship of Scientific Research, Imam Mohammed Ibn Saud Islamic University