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مقدرات بيز وبيز التجريبي لتوزيع لوماكس == Bayes and Empirical Bayes Estimators for Lomax Distribution
Author name:
شهد سعد علوان
Supervisor name:
نادية هاشم النور
General topic:
Mathematics
Specific topic:
Mathematics
Degree:
Master
University:
Mustansiriyah University - College Of Science - Department Of Mathematics
Language:
English
University location:
Baghdad
Abstract:
Point estimation is one of the core topics in mathematical statistics.The main aim of this study is to discuss the most common methods of point estimation : non - Bayes, Bayes and empirical Bayes methods. We consider these estimation methods to estimate the shape parameter, reliability and failure rate functions of Lomax distribution based on complete data. The maximum likelihood, moment and uniformly minimum variance unbiased estimators are obtained as non - Bayes estimators. Bayes and empirical Bayes estimators are obtained corresponding to three informative priors "gamma, chi - square and inverted Levy" based on symmetric "squared error" and asymmetric "LINEX and general entropy" loss functions. Comparisons are made between different estimators empirically via Monte Carlo simulation study. The estimates of the shape parameter were compared based upon the mean squared error while the estimates of reliability and failure rate functions were compared based upon the integrated mean squared error. Among the set of conclusions that have been reached, it is observed that, for all sample sizes and different cases, the performance of uniformly minimum variance unbiased estimator is better than other non - Bayes estimator for estimating the shape parameter and failure rate function of Lomax distribution. Also, it is observed that conjugate gamma prior record full appearance as best prior distribution with Bayes estimates for reliability function. Further that, Monte Carlo simulation results indicate that the performance of Bayes and empirical Bayes estimator for some cases are better than non - Bayes for some appropriate of prior distribution, loss function, values of parameters and sample size.
