الفهرس 
CHAPTER 1 : INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
It is well known that there are many types of ordering the observations in
statistics. Such observations can be obtained from scientific experiments, for
example: ordinary order statistics, sequential order statistics, order statistics
with non integral sample size, ordinary record values, Pfeifer records and
progressive type II censoring order statistics among others. Kamps (1995a)
suggested a new theoretical technique called generalized order statistics.
This model contains all types of ordering mentioned above.
The exponentiated family of distributions contains many exponentiated
distributions such as exponentiated generalized linear exponential,
exponentiated linear failure rate, exponentiated Weibull, exponentiated
modified Weibull, exponentiated Gompertz, exponentiated exponential,
exponentiated Rayleigh, exponentiated Burr type XII, exponentiated Lomax,
exponentiated Pareto and exponentiated Gamma distributions,...etc.
The main purpose of the thesis is to derive recurrence relations for moment,
conditional moment generating functions and product moments of
generalized order statistics from non-truncated and doubly truncated based
on exponentiated family of distributions. This family has been characterized
by using these recurrence relations. Also, Bayesian prediction intervals are
obtained for future generalized order statistics under the exponentiated
family of distributions based on one-sample and two- sample techniques.
We use the Markov Chain Monte Carlo method to compute the Bayes
prediction and to compare these results with the classic Bayesian prediction
method.