Bayes Estimation Based on Joint Progressive Type II Censored Data Under LINEX Loss Function

DOI10.1080/03610918.2012.683921zbMath1301.62101OpenAlexW2039490327MaRDI QIDQ5299928

Mohammad Vali Ahmadi, Mahdi Doostparast, Jafar Ahmadi

Publication date: 24 June 2013

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2012.683921

zbMATH Keywords

proportional hazard modelBayes estimationLINEX loss functionWeibull distributionjoint progressive type II censoring scheme

Mathematics Subject Classification ID

Bayesian inference (62F15) Estimation in survival analysis and censored data (62N02) Reliability and life testing (62N05)

Related Items (11)

Inferences for two Lindley populations based on joint progressive type-II censored data ⋮ Inference for two Lomax populations under joint type-II censoring ⋮ Exact likelihood inference for two exponential populations under jointly generalized progressive hybrid censoring ⋮ Exact inference for joint type-I hybrid censoring model with exponential competing risks data ⋮ On the joint Type-II progressive censoring scheme ⋮ Inferences based on a balanced joint progressive type-II censoring scheme for Lindley distributed lifetimes ⋮ Analysis of two Weibull populations under joint progressively hybrid censoring ⋮ Statistical inference for the jointly adaptive progressive type-II censored Weibull distributions ⋮ The alpha power Muth-G distributions and its applications in survival and reliability analyses ⋮ Point and interval estimation of Weibull parameters based on joint progressively censored data ⋮ Predictive analysis for joint progressive censoring plans: a Bayesian approach

Cites Work

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• Exact Likelihood Inference for Two Exponential Populations Under Joint Progressive Type-II Censoring
• Point estimation under asymmetric loss functions for left-truncated exponential samples
• Weibull Models
• Locally Most Powerful Rank Tests for the Two-sample Problem with Censored Data

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