Bayesian predictive density of order statistics based on finite mixture models |
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| Institution: | 1. Department of Statistics and Operations Research, College of Science, King Saud University, Riyadh, Saudi Arabia;2. Department of Statistics, Faculty of Basic Science and Engineering of Bijar, University of Kurdistan, Bijar, Iran;1. Indian Statistical Institute, Chennai, India;2. Department of Statistics, Cochin University of Science & Technology, Kochi, India;1. Department of Statistics and Operations Research, College of Science, King Saud University, Riyadh, Saudi Arabia;2. Department of Statistics, Faculty of Mathematics and Computing, Higher Education Complex of Bam, Bam, Kerman, Iran;1. Department of Statistics, Faculty of Mathematical Sciences, University of Mazandaran, P.O. Box 47416-1467, Babolsar, Iran;2. Department of Statistics, School of Mathematics, Statistics and Computer Sciences, University of Tehran, P.O. Box 14155-6455, Tehran, Iran;3. Department of Mathematics and Statistics, McMaster University Hamilton, Ontario, Canada L8S 4K1 |
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| Abstract: | Bayesian predictive density functions, which are necessary to obtain bounds for predictive intervals of future order statistics, are obtained when the population density is a finite mixture of general components. Such components include, among others, the Weibull (exponential and Rayleigh as special cases), compound Weibull (three-parameter Burr type XII), Pareto, beta, Gompertz and compound Gompertz distributions. The prior belief of the experimenter is measured by a general distribution that was suggested by AL-Hussaini (J. Statist. Plann. Inf. 79 (1999b) 79). Applications to finite mixtures of Weibull and Burr type XII components are illustrated and comparison is made, in the special cases of the exponential and Pareto type II components, with previous results. |
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