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E-Bayesian Estimation for the Weibull Distribution under Adaptive Type-I Progressive Hybrid Censored Competing Risks Data
by
Okasha, Hassan
, Mustafa, Abdelfattah
in
adaptive type-I progressive hybrid censored
/ Bayesian analysis
/ Bayesian estimation
/ competing risks
/ Computer simulation
/ cumulative exposure model
/ E-Bayesian estimation
/ E-mean-square error
/ Estimators
/ Experiments
/ Failure
/ Lifetime
/ Parameter estimation
/ Weibull distribution
2020
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E-Bayesian Estimation for the Weibull Distribution under Adaptive Type-I Progressive Hybrid Censored Competing Risks Data
by
Okasha, Hassan
, Mustafa, Abdelfattah
in
adaptive type-I progressive hybrid censored
/ Bayesian analysis
/ Bayesian estimation
/ competing risks
/ Computer simulation
/ cumulative exposure model
/ E-Bayesian estimation
/ E-mean-square error
/ Estimators
/ Experiments
/ Failure
/ Lifetime
/ Parameter estimation
/ Weibull distribution
2020
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Do you wish to request the book?
E-Bayesian Estimation for the Weibull Distribution under Adaptive Type-I Progressive Hybrid Censored Competing Risks Data
by
Okasha, Hassan
, Mustafa, Abdelfattah
in
adaptive type-I progressive hybrid censored
/ Bayesian analysis
/ Bayesian estimation
/ competing risks
/ Computer simulation
/ cumulative exposure model
/ E-Bayesian estimation
/ E-mean-square error
/ Estimators
/ Experiments
/ Failure
/ Lifetime
/ Parameter estimation
/ Weibull distribution
2020
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E-Bayesian Estimation for the Weibull Distribution under Adaptive Type-I Progressive Hybrid Censored Competing Risks Data
Journal Article
E-Bayesian Estimation for the Weibull Distribution under Adaptive Type-I Progressive Hybrid Censored Competing Risks Data
2020
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Overview
This article focuses on using E-Bayesian estimation for the Weibull distribution based on adaptive type-I progressive hybrid censored competing risks (AT-I PHCS). The case of Weibull distribution for the underlying lifetimes is considered assuming a cumulative exposure model. The E-Bayesian estimation is discussed by considering three different prior distributions for the hyper-parameters. The E-Bayesian estimators as well as the corresponding E-mean square errors are obtained by using squared and LINEX loss functions. Some properties of the E-Bayesian estimators are also derived. A simulation study to compare the various estimators and real data application is applied to show the applicability of the different estimators are proposed.
Publisher
MDPI AG,MDPI
Subject
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