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Classical and Bayesian Inference Using Type-II Unified Progressive Hybrid Censored Samples for Pareto Model. Appl Bionics Biomech 2022; 2022:2073067. [PMID: 35528527 PMCID: PMC9076315 DOI: 10.1155/2022/2073067] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 02/17/2022] [Accepted: 03/08/2022] [Indexed: 11/18/2022] Open
Abstract
In the lifetime and reliability experiments, the censored samples play a fundamental and important role in order to control time and cost. The researchers developed the censored sample schemes to solve the problems that arise by applying the previous methods. Recently, Górny and Cramer (2018) proposed a new general type of censored sample called Type-II unified progressive hybrid censored sample. In this paper, we present an overview of the Type-II unified progressive hybrid censored sample. We used this censored sample to compute the maximum likelihood estimates of unknown parameters from the Pareto distribution, as well as Bayesian estimates for unknown parameters under three different error loss functions. The point and interval Bayesian predictions one- and two-sample Bayesian predictions from the Pareto distribution are shown. Simulation studies are carried out to compare the efficacy of the various inference approaches. Finally, real data sets are examined to determine the applicability of the proposed model and various estimating approaches.
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Górny J, Cramer E. A volume based approach to establish B-spline based expressions for density functions and its application to progressive hybrid censoring. J Korean Stat Soc 2019. [DOI: 10.1016/j.jkss.2019.04.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Stochastic monotonicity of MLEs of the mean for exponentially distributed lifetimes under hybrid censoring. Stat Probab Lett 2019. [DOI: 10.1016/j.spl.2018.12.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Affiliation(s)
- Benjamin Laumen
- Institute of Statistics, RWTH Aachen University, Aachen, Germany
| | - Erhard Cramer
- Institute of Statistics, RWTH Aachen University, Aachen, Germany
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