In this paper simulation technique plays a vital role to compare between two approaches Maximum Likelihood method and Developed Least Square method to estimate the parameters of Frechet Poisson Lindley Distribution Compound. by coding using Matlab software program. Also, under different sample sizes via mean square error. As the results which obtain that Maximum Likelihood Estimation method is better than Developed Least Square method to estimate these parameters to the proposed distribution.
In this paper, the reliability of the stress-strength model is derived for probability P(Y<X) of a component having its strength X exposed to one independent stress Y, when X and Y are following Gompertz Fréchet distribution with unknown shape parameters and known parameters . Different methods were used to estimate reliability R and Gompertz Fréchet distribution parameters, which are maximum likelihood, least square, weighted least square, regression, and ranked set sampling. Also, a comparison of these estimators was made by a simulation study based on mean square error (MSE) criteria. The comparison confirms that the performance of the maximum likelihood estimator is better than that of the other estimators.
The shape for even-even (54Xe 118≤ A ≤ 140 and 82Pb 204 ≤ A ≤ 210 ) nuclei have been studied and investigated through the deformation parameters and δ , these deformation parameters were calculated by two different methods. The first one is nucleus quadrupole deformation parameter β2 from reduced transition probability B(E2)↑ for 0+→2+1 transitions and the second is nucleus quadrupole deformation parameters δ from quadrupole moment Qo.The relationship between two deformation parameters ( , ) and neutrons magic number (N=82 & 126) was studied through plotting the deformation parameters ( , ) as a function of neutrons number , from this relationship we can see very cleary that the deformation of nucleus decreased when th
... Show MoreThis Book is the second edition that intended to be textbook studied for undergraduate/ postgraduate course in mathematical statistics. In order to achieve the goals of the book, it is divided into the following chapters. Chapter One introduces events and probability review. Chapter Two devotes to random variables in their two types: discrete and continuous with definitions of probability mass function, probability density function and cumulative distribution function as well. Chapter Three discusses mathematical expectation with its special types such as: moments, moment generating function and other related topics. Chapter Four deals with some special discrete distributions: (Discrete Uniform, Bernoulli, Binomial, Poisson, Geometric, Neg
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