TY - JOUR JF - srtc-jsri JO - JSRI VL - 12 IS - 2 PY - 2016 Y1 - 2016/3/01 TI - Bayesian Two-sample Prediction with Progressively Censored Data for Generalized Exponential Distribution Under Symmetric and Asymmetric Loss Functions TT - پیش‌بینی دو‌نمونه‌ای بیزی در داده‌های سانسورشده‌ی فزاینده از توزیع نمایی تعمیم‌یافته، تحت تابع‌های زیان متقارن و نامتقارن N2 - 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. SP - 179 EP - 204 AU - Ghafouri, S. AU - Habibi Rad, A. AU - Doostparast, M. AD - KW - Bayesian prediction KW - generalized exponential model KW - gibbs sampling KW - LINEX loss function KW - Markov Chain Monte Carlo KW - progressive type-II censoring scheme KW - two-sample prediction. UR - http://jsri.srtc.ac.ir/article-1-193-en.html DO - 10.18869/acadpub.jsri.12.2.179 ER -