Open Journal of Statistics

Volume 2, Issue 3 (July 2012)

ISSN Print: 2161-718X   ISSN Online: 2161-7198

Google-based Impact Factor: 0.78  Citations  h5-index & Ranking

Prediction Based on Generalized Order Statistics from a Mixture of Rayleigh Distributions Using MCMC Algorithm

HTML  Download Download as PDF (Size: 315KB)  PP. 356-367  
DOI: 10.4236/ojs.2012.23044    4,376 Downloads   8,551 Views  Citations

ABSTRACT

This article considers the problem in obtaining the maximum likelihood prediction (point and interval) and Bayesian prediction (point and interval) for a future observation from mixture of two Rayleigh (MTR) distributions based on generalized order statistics (GOS). We consider one-sample and two-sample prediction schemes using the Markov chain Monte Carlo (MCMC) algorithm. The conjugate prior is used to carry out the Bayesian analysis. The results are specialized to upper record values. Numerical example is presented in the methods proposed in this paper.

Share and Cite:

T. Abushal and A. Al-Zaydi, "Prediction Based on Generalized Order Statistics from a Mixture of Rayleigh Distributions Using MCMC Algorithm," Open Journal of Statistics, Vol. 2 No. 3, 2012, pp. 356-367. doi: 10.4236/ojs.2012.23044.

Cited by

[1] Reliability analysis of three-component mixture of distributions
2018
[2] Bayesian Prediction of Future Generalized Order Statistics from a Class of Finite Mixture Distributions
Open Journal of Statistics, 2015
[3] تاثير المزيج التسويقي في ادارة علاقات الزبون-دراسة استطلاعية لاراء عينة في شركة نصر العامة للصناعات الميكانيكية-بغداد‎
2014
[4] مقارنة طريقة الامكان الاعظم مع طرائق اخرى لتقدير معلمة الشكل لتوزيع رايلي العام باستخدام المحاكاة‎
2014
[5] Bayesian Estimation of Rayleigh Distribution Based on Generalized Order Statistics
Applied Mathematical Sciences, 2014
[6] Inferences under a Class of Finite Mixture Distributions Based on Generalized Order Statistics
Open Journal of Statistics, 2013

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.