Reliability Estimation for Dependent Left-Truncated and Right-Censored Competing Risks Data with Illustrations

Zhiyuan Zuo, Liang Wang () and Yuhlong Lio
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Zhiyuan Zuo: School of Mathematics, Yunnan Normal University, Kunming 650500, China
Liang Wang: School of Mathematics, Yunnan Normal University, Kunming 650500, China
Yuhlong Lio: Department of Mathematical Sciences, University of South Dakota, Vermillion, SD 57069, USA

Energies, 2022, vol. 16, issue 1, 1-25

Abstract: In this paper, a competing risks model with dependent causes of failure is considered under left-truncated and right-censoring scenario. When the dependent failure causes follow a Marshall–Olkin bivariate exponential distribution, estimation of model parameters and reliability indices are proposed from classic and Bayesian approaches, respectively. Maximum likelihood estimators and approximate confidence intervals are constructed, and conventional Bayesian point and interval estimations are discussed as well. In addition, E-Bayesian estimators are proposed and their asymptotic behaviors have been investigated. Further, another objective-Bayesian analysis is also proposed when a noninformative probability matching prior is used. Finally, extensive simulation studies are carried out to investigate the performance of different methods. Two real data examples are presented to illustrate the applicability.

Keywords: dependence competing risks; left-truncated and right-censored data; bivariate exponential distribution; E-Bayesian estimation; objective Bayesian analysis; posterior analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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