Parameter Estimation of Type-II Hybrid Censored Weighted Exponential Distribution

TL;DR

This paper develops and compares statistical methods for estimating parameters of a weighted exponential distribution under Type-II hybrid censoring, using EM algorithm, Bayesian techniques, and simulations.

Contribution

It introduces a comprehensive approach combining EM, Bayesian, and simulation methods for parameter estimation in hybrid censored weighted exponential distributions.

Findings

Maximum likelihood estimators evaluated via EM algorithm.

Bayesian estimators obtained using Markov Chain Monte Carlo.

Performance comparison through Monte Carlo simulations.

Abstract

A hybrid censoring scheme is a mixture of Type-I and Type-II censoring schemes. We study the estimation of parameters of weighted exponential distribution based on Type-II hybrid censored data. By applying EM algorithm, maximum likelihood estimators are evaluated. Also using Fisher infirmation matrix asymptotic confidence intervals are provided. By applying Markov Chain Monte Carlo techniques Bayes estimators, and corresponding highest posterior density confidence intervals of parameters are obtained. Monte Carlo simulations to compare the performances of the different methods is performed and one data set is analyzed for illustrative purposes.