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Prediction Intervals for Future Order Statistics from a Generalized Modified Weibull.
- Source :
-
Communications in Statistics: Simulation & Computation . 2016, Vol. 45 Issue 10, p3508-3527. 20p. - Publication Year :
- 2016
-
Abstract
- Viewing the future order statistics as latent variables at each Gibbs sampling iteration, several Bayesian approaches to predict future order statistics based on type-II censored order statistics, X(1),X(2), . . ., X(r), of a size n(>r) random sample from a four-parameter generalized modifiedWeibull (GMW) distribution, are studied. Four parameters of the GMW distribution are first estimated via simulation study. Then various Bayesian approaches, which include the plug-in method, the Monte Carlo method, the Gibbs sampling scheme, and the MCMC procedure, are proposed to develop the prediction intervals of unobserved order statistics. Finally, four type-II censored samples are utilized to investigate the predictions. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 03610918
- Volume :
- 45
- Issue :
- 10
- Database :
- Academic Search Index
- Journal :
- Communications in Statistics: Simulation & Computation
- Publication Type :
- Academic Journal
- Accession number :
- 117874961
- Full Text :
- https://doi.org/10.1080/03610918.2014.1002847