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تقدير MININMAX لمعلمة توزيع ماكسويل باستخدام دوال خسارة مختلفة == Minimax Estimation of The Parameter of The Maxwell Distribution Using Different Loss Functions
اسم المؤلف:
زينب نعيم خليفة
اسم المشرف:
هدى عبد الله رشيد
الموضوع العام:
علوم الرياضيات
السنة:
2017
الموضوع الدقيق:
الرياضيات
الدرجة:
ماجستير
الجامعة:
الجامعة المستنصرية - كلية العلوم - قسم الرياضيات
اللغة:
الانكليزية
مكان الجامعة:
بغداد
الصفحات الاولى:
📎 27T1147 - p.pdf
المستخلص:
The Maxwell distribution is a probability distribution with application in physics and chemistry. The most frequent application is in the field of statistical mechanics. Hence, the importance of this study to find best estimators for scale parameter of the Maxwell distribution when there is unknown and so to obtain through Classical estimators(Maximumlikelihood estimators, Uniformly minimum variance unbiased estimator, and Minimum mean squared error estimator), as well as Bayesian estimators using different loss functions represented by Quadratic loss function, Precautionary loss function and Generalized Weighted loss function. And in order to get a better understanding of our Bayesian analysis, we consider the non - informative prior for the scale parameter ( ) using Jefferys prior information as well as informative priors represented by Gumbel Type II prior, nverted Gamma prior and Inverted Levy prior.All these estimators are compared empirically using Mont - Carlo simulation by employing the mean squared errors (MSE's). After that, we derived the better Minimax estimators of the scale parameter for the Maxwell distribution for all loss functions referredabove and reach to the estimators by using Quadratic loss function, and the special case of Generalized Weighted loss function, are Minimax estimators, as for Precautionary loss function is not minimax estimator.Among conclusions that have been reached, The performance of Bayes estimates using generalized weighted loss function based on Inverted Gamma prior information(GWIG03) when(k=0, c=3), is better than the performance of corresponding estimate based on Jefferys prior noninformation, Gumbel type II prior information, and Inverted Levy prior information in most cases.
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