مشاركة
مقارنة المقدرات الكلاسيكية والبيزية للتوزيع الاسي المعكوس == Comparison Classical and Bayesian Estimators for the Inverted Exponential Distribution
اسم المؤلف:
سوزان فرمان باوي
اسم المشرف:
نادية هاشم النور
الموضوع العام:
علوم الرياضيات
السنة:
2015
الموضوع الدقيق:
الرياضيات
الدرجة:
ماجستير
الجامعة:
الجامعة المستنصرية - كلية العلوم - قسم الرياضيات
اللغة:
الانكليزية
مكان الجامعة:
بغداد
المستخلص:
The inverted exponential distribution is a member of continuous probability distributions. It has been introduced by Keller and Kamath in (1982) when they studied the shapes of the density and failure rate functions for the basic inverse model. Recently, inverted exponential distribution has been received attention from many researchers.This study is devoted to discuss the classical and Bayesian estimation problem of the unknown parameter of inverted exponential distribution. Maximum likelihood estimator is obtained as classical estimation. Bayes estimators are obtained corresponding to informative and non - informative priors "inverted gamma, Gumbel type II, Jeffrey and extension of Jeffrey" under four symmetric and asymmetric loss functions. Also, by using Lehmann’s theorem, Bayes estimators are examined if it minimax estimators, semi - minimax estimators or not. The obtained maximum likelihood estimator along with Bayes estimators are compared empirically for different cases and multiple sample sizes using Monte - Carlo simulation method in terms of two statistical criteria which are mean squared error (MSE) and mean absolute percentage error (MAPE). Among the set of conclusions that have been reached, it is observed that, Bayes estimate for the parameter corresponding to inverted gamma prior with hyper - parameters under generalentropy loss function with large (positive or negative) value of represent the best estimate when and respectively for all sample sizes. Also, it is observed that non - informative Jeffrey's prior and non - informative extension of Jeffrey's prior with extension constant equal to one didn't record any appearance as best prior.
