Prediction Based on Generalized Order Statistics from a Mixture of Rayleigh Distributions Using MCMC AlgorithmReport as inadecuate




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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.

KEYWORDS

Mixture Distributions; Rayleigh Distribution; Generalized Order Statistics; Record Values; MCMC

Cite this paper

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.





Author: Tahani A. Abushal, Areej M. Al-Zaydi

Source: http://www.scirp.org/



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