Statistical inferences for the reliability functions in the proportional hazard rate models based on progressive type-II right censoring

The aim of this paper is to develop the estimation procedures for the reliability functions R(t) and P, when the available data have the form of progressively type-II right censored sample. It is supposed that X-sample and X*-sample are independent and generated from the two different proportional h...

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Bibliographic Details
Published in:Journal of statistical computation and simulation 2019-08, Vol.89 (12), p.2187-2217
Main Authors: Chaturvedi, Ajit, Pathak, Anupam, Kumar, Narendra
Format: Article
Language:English
Subjects:
asymptotic confidence interval
Asymptotic methods
Bayesian analysis
Computer simulation
Confidence intervals
Economic models
Estimators
Monte Carlo simulation
Parameters
point estimation
Progressive type-II right censoring
proportional hazard rate model
Reliability
Statistical analysis
Statistical methods
Statistical models
Test procedures
testing procedures
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Summary:The aim of this paper is to develop the estimation procedures for the reliability functions R(t) and P, when the available data have the form of progressively type-II right censored sample. It is supposed that X-sample and X*-sample are independent and generated from the two different proportional hazard rate models, respectively, with different probability parameters which covers many lifetime distributions as specific cases [see Basirat M, Baratpour S, Jafar A. Statistical inferences for the proportional hazard models based on progressive type-II censored samples. J Stat Comput Simul. 2014. Available from: http://www.tandfonline.com/loi/gscs20 ]. Point estimation (classical as well as Bayesian) procedures and asymptotic confidence intervals are developed for parameter α, and . Testing procedures are also developed for the hypotheses related to various parametric functions. The performances of these estimators are studied by using Monte Carlo simulation technique. Finally, the use of the proposed estimators is shown in an illustrative example.
ISSN:0094-9655
1563-5163
DOI:10.1080/00949655.2019.1614182