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E-Bayesian Estimation for the Weibull Distribution under Adaptive Type-I Progressive Hybrid Censored Competing Risks

Hassan Okasha1,2, Abdelfattah Mustafa3,4

  • 1Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary

This study introduces E-Bayesian estimation for Weibull distribution under adaptive type-I progressive hybrid censored competing risks. It provides a novel approach for analyzing complex reliability data using various prior and loss functions.

Keywords:
Bayesian estimationE-Bayesian estimationE-mean-square erroradaptive type-I progressive hybrid censoredcompeting riskscumulative exposure model

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Area of Science:

  • Statistics
  • Reliability Engineering
  • Survival Analysis

Background:

  • Competing risks analysis is crucial for understanding failure patterns.
  • Weibull distribution is widely used for modeling lifetime data.
  • Progressive censoring schemes offer efficient data collection in reliability studies.

Purpose of the Study:

  • To develop and evaluate E-Bayesian estimators for the Weibull distribution.
  • To analyze data from adaptive type-I progressive hybrid censored competing risks (AT-I PHCS) setups.
  • To compare the performance of different estimators under various conditions.

Main Methods:

  • Utilized E-Bayesian estimation techniques.
  • Employed three distinct prior distributions for hyper-parameters.
  • Applied squared and LINEX loss functions.
  • Conducted a simulation study for performance comparison.
  • Included a real data application for practical validation.

Main Results:

  • Derived E-Bayesian estimators and their mean squared errors.
  • Demonstrated the applicability of the proposed estimators through simulations.
  • Validated the practical utility of the methods with a real-world dataset.

Conclusions:

  • The proposed E-Bayesian estimation method is effective for Weibull distribution under AT-I PHCS.
  • The study provides valuable insights into competing risks analysis with censored data.
  • The findings support the use of these estimators in reliability and survival analysis.