The main object of this book is to make statistical inferences
(recurrence relations, estimation and prediction) for inverse
Weibull model using generalized order statistics. This book has
been organized and presented in six Chapters. Basic concepts and
some other definitions and notations are given. A review of some of
the work done concerning the generalized order statistics
(progressive censored data, ordinary order statistics and lower
k-record values) on the recurrence relations, the Bayesian and non-
Bayesian approaches are given. We are concerned with the problem of
estimation of the parameters and the reliability function of
inverse Weibull model based on generalized order statistics. For
this purpose, the maximum likelihood and Bayes estimators are used.
Bayes estimators with respect to balanced squared error loss
function and Balanced LINEX loss function are obtained. This was
done under assumption of discrete-continuous mixture prior for the
unknown model parameters. A Bayesian approach using Markov chain
Monte Carlo techniques to generate from the posterior distributions
is also developed. Our results are specialized to Progressively
Type-II censored data.
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