# Bayesian and Frequentist Two-Sample Predictions of the Inverse Weibull Model Based on Generalized Order Statistics

• Volume: 37, Issue: 1, page 45-66
• ISSN: 1310-6600

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## Abstract

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2000 Mathematics Subject Classification: 62E16,62F15, 62H12, 62M20.This paper is concerned with the problem of deriving Bayesian prediction bounds for the future observations (two-sample prediction) from the inverse Weibull distribution based on generalized order statistics (GOS). Study the two side interval Bayesian prediction, point prediction under symmetric and asymmetric loss functions and the maximum likelihood (ML) prediction using "plug-in" procedure for future observations from the inverse Weibull distribution based on GOS. Study the problem of predicting future records based on observed progressive type II censored data and observed order statistics from the inverse Weibull distribution. Finally, a numerical example using real data are used to illustrate the procedure.

## How to cite

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Abd Ellah, A. H.. "Bayesian and Frequentist Two-Sample Predictions of the Inverse Weibull Model Based on Generalized Order Statistics." Serdica Mathematical Journal 37.1 (2011): 45-66. <http://eudml.org/doc/281572>.

@article{AbdEllah2011,
abstract = {2000 Mathematics Subject Classification: 62E16,62F15, 62H12, 62M20.This paper is concerned with the problem of deriving Bayesian prediction bounds for the future observations (two-sample prediction) from the inverse Weibull distribution based on generalized order statistics (GOS). Study the two side interval Bayesian prediction, point prediction under symmetric and asymmetric loss functions and the maximum likelihood (ML) prediction using "plug-in" procedure for future observations from the inverse Weibull distribution based on GOS. Study the problem of predicting future records based on observed progressive type II censored data and observed order statistics from the inverse Weibull distribution. Finally, a numerical example using real data are used to illustrate the procedure.},
author = {Abd Ellah, A. H.},
journal = {Serdica Mathematical Journal},
keywords = {Prediction; Inverse Wiebull; Generalized Order Statistics; Record Values; Progressive Type II Censored; Symmetric Loss Functions; Asymmetric Loss Functions; prediction; record values; progressive Type II censoring; symmetric and asymmetric loss functions},
language = {eng},
number = {1},
pages = {45-66},
publisher = {Institute of Mathematics and Informatics Bulgarian Academy of Sciences},
title = {Bayesian and Frequentist Two-Sample Predictions of the Inverse Weibull Model Based on Generalized Order Statistics},
url = {http://eudml.org/doc/281572},
volume = {37},
year = {2011},
}

TY - JOUR
AU - Abd Ellah, A. H.
TI - Bayesian and Frequentist Two-Sample Predictions of the Inverse Weibull Model Based on Generalized Order Statistics
JO - Serdica Mathematical Journal
PY - 2011
PB - Institute of Mathematics and Informatics Bulgarian Academy of Sciences
VL - 37
IS - 1
SP - 45
EP - 66
AB - 2000 Mathematics Subject Classification: 62E16,62F15, 62H12, 62M20.This paper is concerned with the problem of deriving Bayesian prediction bounds for the future observations (two-sample prediction) from the inverse Weibull distribution based on generalized order statistics (GOS). Study the two side interval Bayesian prediction, point prediction under symmetric and asymmetric loss functions and the maximum likelihood (ML) prediction using "plug-in" procedure for future observations from the inverse Weibull distribution based on GOS. Study the problem of predicting future records based on observed progressive type II censored data and observed order statistics from the inverse Weibull distribution. Finally, a numerical example using real data are used to illustrate the procedure.
LA - eng
KW - Prediction; Inverse Wiebull; Generalized Order Statistics; Record Values; Progressive Type II Censored; Symmetric Loss Functions; Asymmetric Loss Functions; prediction; record values; progressive Type II censoring; symmetric and asymmetric loss functions
UR - http://eudml.org/doc/281572
ER -

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