الفهرس 
ABSTRACT
Basic ConceptsOnly 14 pages are availabe for public view
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Abstract
The main purpose of this thesis is to make statistical inferences (estimations and predictions ) for a class of finite mixture distributions based on generalized hybrid censoring schemes of generalized order statistics. The thesis contains some basic concepts of generalized order statistics, frequentist approach in estimation, Bayesian approach in estimation, Bayesian prediction, the class of continuous distributions and a finite mixture of two components of the class. Also, find maximum likelihood and Bayes estimates by using Monte Carlo integration method and Markov chain Monte Carlo algorithm of the parameters, reliability and hazard rate functions. Bayesian prediction intervals are obtained for future generalized order statistics from a mixture of two general components based on generalized hybrid censoring schemes. We consider one-sample and two-sample prediction techniques using the classical Bayesian prediction and MCMC technique. Illustrative example of finite mixture of two Weibull components is given. Our results are specialized to special cases of generalized order statistics. Also, we give an example based on real data.