:: Volume 12, Issue 2 (3-2016) ::
JSRI 2016, 12(2): 179-204 Back to browse issues page
Bayesian Two-sample Prediction with Progressively Censored Data for Generalized Exponential Distribution Under Symmetric and Asymmetric Loss Functions
S. Ghafouri, A. Habibi Rad *1, M. Doostparast
1- , ahabibi@um.ac.ir
Abstract:   (3481 Views)

Received: 4/12/2015            Approved: 2/6/2016‎

Statistical prediction analysis plays an important role in a wide range of fields. Examples include engineering systems, design of experiments, etc. In this paper, based on progressively Type-II right censored data, Bayesian two-sample point and interval predictors are developed under both informative and non-informative priors. By assuming a generalized exponential model, prediction bounds as well as Bayes point predictors are obtained under the squared error loss (SEL) and the Linear-Exponential (LINEX) loss functions for the order statistic in a future progressively Type-II censored sample with an arbitrary progressive censoring scheme. The derived results may be used for prediction of total time on test in lifetime experiments. %in reliability analyses In addition to numerical method, Gibbs sampling procedure (as Markov Chain Monte Carlo method) are used to assess approximate prediction bounds and Bayes point predictors under the SEL and LINEX loss functions. The performance of the proposed prediction procedures are also demonstrated via a Monte Carlo simulation study and an illustrative example, for each method.

Keywords: Bayesian prediction, generalized exponential model, gibbs sampling, LINEX loss function, Markov Chain Monte Carlo, progressive type-II censoring scheme, two-sample prediction.
Full-Text [PDF 223 kb]   (897 Downloads)    
Type of Study: Research | Subject: General
Received: 2016/07/4 | Accepted: 2016/07/4 | Published: 2016/07/4

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Volume 12, Issue 2 (3-2016) Back to browse issues page