8th world congress in probability and statistics , 2012-07-09

Title : ( Comparison of various progressive Type-II censoring schemes in Bayesian two-sample prediction for Generalized Exponential Distribution )

Authors: Somayeh Ghafouri , Arezou Habibirad ,

Citation: BibTeX | EndNote

Statistical prediction analysis plays an important role in various fields including medical and engineering sciences. In this paper, based on progressively Type-II right censored data, we shall consider Bayesian two-sample prediction. Generalized exponential (GE) model, which has been introduced as a quite effective model for lifetime data analysis instead of the Weibull or gamma distributions, is considered for obtaining prediction bounds for the s-th order statistic in a future random sample drawn independently and with different and arbitrary progressive censoring schemes. Finally, we carry out a simulation study to assess the computational comparison of coverage probabilities in different progressive Type-II censoring schemes based on Bayesian two-sample prediction. In addition, we present schemes which are not suitable for predicting the s-th order statistic in a future random sample drawn from GE model.

Keywords

, Bayesian prediction bounds; Generalized exponential (GE) model; Progressive Type, II right censoring scheme; Two, sample
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@inproceedings{paperid:1035222,
author = {Ghafouri, Somayeh and Habibirad, Arezou},
title = {Comparison of various progressive Type-II censoring schemes in Bayesian two-sample prediction for Generalized Exponential Distribution},
booktitle = {8th world congress in probability and statistics},
year = {2012},
location = {استانبول},
keywords = {Bayesian prediction bounds; Generalized exponential (GE) model; Progressive Type-II right censoring scheme; Two-sample prediction.},
}

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%0 Conference Proceedings
%T Comparison of various progressive Type-II censoring schemes in Bayesian two-sample prediction for Generalized Exponential Distribution
%A Ghafouri, Somayeh
%A Habibirad, Arezou
%J 8th world congress in probability and statistics
%D 2012

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