الفهرس 
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Abstract
Introducing and studying generalized distributions represent a real challenge for the statisticians. These generalized distributions have a lot of applications in several areas such as: childhood cancer, dependence of chronic obstructive respiratory disease prevalence on smoking and age, geological issues, growth of human population, hemolytic uremic syndrome data analysis, physiochemical phenomenon, pneumoconiosis in coal miners, psychological issues, study of family diseases, survival of diagnosed leukemia patients, weight gain analysis, finance, economics and others. The increasing of applications in these areas have forced the need for more models of generalized distributions. In this thesis, we introduce generalized distributions by using two approaches. The first one is called the Exponentiated method [43,45,65,77] and the second approach is called the Exponential method [53], which enables us to add a parameter to any family of distributions. We applied the first method on Exponential Pareto distribution and the second method on the Lomax distribution. Furthermore, we investigate some reliability properties of the new families of distributions. Quantiles and hazard rate function are obtained. Moments and mean deviation are provided. This thesis consists of three chapters as follows: The first chapter entitled ”Introduction” is divided into seven sections. In the first section, the importance of the studied problem is presented. The basic definitions and main concepts of statistical distributions are re- viewed in Section 2. In Section 3, the basic definitions of some special functions are presented. The mostly common techniques of estimation namely, moment method estimate (MME) and the maximum likelihood method estimate (MLE) are presented in Section 4. In Section 5, The distributions related to our work are given. Historical and scientific back- ground are presented in Section 6. In Section 7, the aim of the work is addressed. The second chapter entitled ”Exponentiated Exponential Pareto distribution” is divided into eleven sections. A new generalization of Exponential Pareto distribution called Exponentiated Exponential Pareto (EEP) distribution is introduced in Section 2. Various properties including quantiles and the hazard function are investigated in Section 3. The moments of the EEP are calculated in Section 4. The incomplete moments and probability weighted moments are provided in Sections 5 and 6 respectively. The rényi entropies are calculated in Section 7. The mean deviation from the mean and the mean deviation from the median are provided in Section 8. Section 9 is devoted to the maximum likelihood estimates of the parameters of the EEP. In Section 10, an application of the EEP to an annual flood discharge rates of the floyd river data set is provided. In Section 11, the conclusion of the chapter is presented. The third chapter entitled ”Exponential Lomax distribution” is divided into twelve sections. A new generalization of the lomax distribution called Exponential lomax (ELomax) distribution is introduced in Section 2. Various properties including quantiles and the hazard function are investigated in Section 3. The moments of the ELomax are calculated in Section 4. The incomplete moments and probability weighted moments are provided in Sections 5 and 6 respectively. The mean deviation from the mean and the mean deviation from the median are provided in Section 7. The rényi entropies are calculated in Section 8. TL-moments and L-moments for the ELomax distribution are computed in section 9. Section 10 is devoted to the maximum likelihood estimates of the parameters of the ELomax. In Section 11, an application of the ELomax to a failure times of 84 Aircraft Windshield data set is provided. In Section 12, the conclusion of the chapter is presented.