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Inference of progressively type-II censored competing risks data from Chen distribution with an application.

Essam A Ahmed1,2, Ziyad Ali Alhussain3, Mukhtar M Salah3

  • 1Department of Administrative and Financial Sciences, Taibah University, Community College of Khyber, Madinah, Saudi Arabia.

Journal of Applied Statistics
|June 16, 2022
PubMed
Summary
This summary is machine-generated.

This study estimates Chen distribution parameters under competing risks using progressive censoring. Both frequentist and Bayesian methods, including maximum likelihood estimation and Markov chain Monte Carlo, were employed for robust analysis.

Keywords:
62F1062F1562F40Bayesian estimationCompeting risksEM algorithmMarkov chain Monte Carlobootstrapprogressively type-II censoring

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Area of Science:

  • Reliability Engineering
  • Statistical Inference
  • Survival Analysis

Background:

  • The Chen distribution is a flexible model for lifetime data.
  • Competing risks and progressive censoring are common in real-world reliability scenarios.
  • Accurate parameter estimation is crucial for reliability assessment.

Purpose of the Study:

  • To estimate unknown parameters of the Chen distribution under progressive Type-II censoring with competing failure causes.
  • To compare frequentist and Bayesian estimation approaches.
  • To evaluate the performance of different estimation and confidence interval methods.

Main Methods:

  • Maximum Likelihood Estimation (MLE) using the Expectation-Maximization (EM) algorithm.
  • Computation of the expected Fisher information matrix.
  • Asymptotic and bootstrap confidence intervals (Bootstrap-p, Bootstrap-t).
  • Bayesian estimation via Markov Chain Monte Carlo (MCMC) with various loss functions (squared error, LINEX, general entropy).

Main Results:

  • The study provides point and interval estimates for Chen distribution parameters under complex censoring and competing risks.
  • Performance evaluation through simulation demonstrates the effectiveness of the proposed methods.
  • A real-life example illustrates the practical application of the developed techniques.

Conclusions:

  • Both frequentist and Bayesian approaches offer viable methods for parameter estimation in this setting.
  • The simulation study aids in selecting appropriate estimators and confidence intervals based on desired properties.
  • The findings contribute to improved reliability analysis in the presence of progressive censoring and competing failure modes.