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العنوان
Unified treatment of life distributions /
الناشر
Wafaa Yahia Ahmed ,
المؤلف
Wafaa Yahia Ahmed
هيئة الاعداد
باحث / Wafaa Yahia Ahmed
مشرف / Abdel- Hadi Nabih Ahmed
مشرف / Hiba Zeyada Muhammed
مشرف / Ahmed Ramses
تاريخ النشر
2020
عدد الصفحات
134 Leaves :
اللغة
الإنجليزية
الدرجة
الدكتوراه
التخصص
الإحصاء والاحتمالات
تاريخ الإجازة
1/1/2020
مكان الإجازة
اتحاد مكتبات الجامعات المصرية - Mathematical Statistics
الفهرس
Only 14 pages are availabe for public view

from 150

from 150

Abstract

Statistical distributions are used to model real life phenomena. In many applied sciences such as medicine, engineering and finance, amongst others, modeling and analyzing lifetime data are crucial. Several lifetime distributions have been used to model such data.The quality of the procedures used in a statistical analysis depends heavily on the assumed probability model. Seeking flexibility of modeling different types of phenomena remains a strong reason for developing new distributions. Since there is a clear need for extended forms of these distributions, a significant progress has been made towards the generalization of some well-known distributions and their successful applications in different problems. Although, the previous efforts have resulted in more flexible distributions, still remains many important problems where the real data does not follow any of the classical or the extended probability models.The main aim of this thesis is to introduce a new generator.The new generator, based on the star-shaped property, grantees the existences of some well know properties for the generated classes and distributions for any non-negative random variables. The new class is named the composed -GQ class.To examine the performance of the new generator and the generated models in fitting several data, a new produced model called composed- inverted generalized exponential- exponential is derived and compared with some well-known models to fit different types of data. This comparison has shown that the introduced model based on the newly suggested generator has resulted in the best fit to all sets of data