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jeasiq-1466
تقدير معلمتي التوزيع الاسي العام باستخدام اسلوب المحاكاة
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The main aim of this paper is to study how the different estimators of the two unknown parameters (shape and scale parameter) of a generalized exponential distribution behave for different sample sizes and for different parameter values. In particular, 

. Maximum Likelihood, Percentile and Ordinary Least Square estimators had been implemented for different sample sizes (small, medium, and large) and assumed several contrasts initial values for the two parameters. Two indicators of performance Mean Square Error and Mean Percentile Error were used and the comparisons were carried out between different methods of estimation  by using monte carlo simulation technique .. It was observed from the results that the PCE method had a better performance than the other methods for different sample sizes.

                                   



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Publication Date
Wed Feb 10 2016
Journal Name
ألمؤتمر الدولي العلمي الخامس للاحصائيين العرب/ القاهرة
Proposition of Modified Genetic Algorithm to Estimate Additive Model by using Simulation
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Often phenomena suffer from disturbances in their data as well as the difficulty of formulation, especially with a lack of clarity in the response, or the large number of essential differences plaguing the experimental units that have been taking this data from them. Thus emerged the need to include an estimation method implicit rating of these experimental units using the method of discrimination or create blocks for each item of these experimental units in the hope of controlling their responses and make it more homogeneous. Because of the development in the field of computers and taking the principle of the integration of sciences it has been found that modern algorithms used in the field of Computer Science genetic algorithm or ant colo

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Publication Date
Fri Sep 30 2022
Journal Name
Journal Of Economics And Administrative Sciences
Comparison of Some Methods for Estimating the Survival Function and Failure Rate for the Exponentiated Expanded Power Function Distribution
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     We have presented the distribution of the exponentiated expanded power function (EEPF) with four parameters, where this distribution was created by the exponentiated expanded method created by the scientist Gupta to expand the exponential distribution by adding a new shape parameter to the cumulative function of the distribution, resulting in a new distribution, and this method is characterized by obtaining a distribution that belongs for the exponential family. We also obtained a function of survival rate and failure rate for this distribution, where some mathematical properties were derived, then we used the method of maximum likelihood (ML) and method least squares developed  (LSD)

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Publication Date
Sun Apr 01 2018
Journal Name
Journal Of Economics And Administrative Sciences
Determine Optimal Preventive Maintenance Time Using Scheduling Method
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In this paper, the reliability and scheduling of maintenance of some medical devices were estimated by one variable, the time variable (failure times) on the assumption that the time variable for all devices has the same distribution as (Weibull distribution.

The method of estimating the distribution parameters for each device was the OLS method.

The main objective of this research is to determine the optimal time for preventive maintenance of medical devices. Two methods were adopted to estimate the optimal time of preventive maintenance. The first method depends on the maintenance schedule by relying on information on the cost of maintenance and the cost of stopping work and acc

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Publication Date
Sun Apr 01 2018
Journal Name
Journal Of Economics And Administrative Sciences
Estimate Kernel Ridge Regression Function in Multiple Regression
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             In general, researchers and statisticians in particular have been usually used non-parametric regression models when the parametric methods failed to fulfillment their aim to analyze the models  precisely. In this case the parametic methods are useless so they turn to non-parametric methods for its easiness in programming. Non-parametric methods can also used to assume the parametric regression model for subsequent use. Moreover, as an advantage of using non-parametric methods is to solve the problem of Multi-Colinearity between explanatory variables combined with nonlinear data. This problem can be solved by using kernel ridge regression which depend o

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Publication Date
Thu Jun 30 2022
Journal Name
Journal Of Economics And Administrative Sciences
Using Genetic Algorithm to Estimate the Parameters of the Gumbel Distribution Function by Simulation
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In this research, the focus was on estimating the parameters on (min- Gumbel distribution), using the maximum likelihood method and the Bayes method. The genetic algorithmmethod was employed in estimating the parameters of the maximum likelihood method as well as  the Bayes method. The comparison was made using the mean error squares (MSE), where the best  estimator  is the one who has the least mean squared error. It was noted that the best estimator was (BLG_GE).

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Publication Date
Tue Dec 01 2009
Journal Name
Journal Of Economics And Administrative Sciences
Estimation of the parameter of the Pareto distribution manual Using the general mediator estimator
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Estimation of the tail index parameter of a one - parameter Pareto model has wide important by the researchers because it has awide application in the econometrics science and reliability theorem.

Here we introduce anew estimator of "generalized median" type and compare it with the methods of Moments and Maximum likelihood by using the criteria, mean square error.

 The estimator of generalized median type performing best over all.

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Publication Date
Wed Nov 01 2017
Journal Name
Journal Of Economics And Administrative Sciences
strong criminal capabilities، Using simulation .
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The penalized least square method is a popular method to deal with high dimensional data ,where  the number of explanatory variables is large than the sample size . The properties of  penalized least square method are given high prediction accuracy and making estimation and variables selection

 At once. The penalized least square method gives a sparse model ,that meaning a model with small variables so that can be interpreted easily .The penalized least square is not robust ,that means very sensitive to the presence of outlying observation , to deal with this problem, we can used a robust loss function to get the robust penalized least square method ,and get robust penalized estimator and

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Publication Date
Wed Oct 20 2004
Journal Name
Al-academy
الانتقالات الفنية في تكوين اسلوب الفنان حافظ الدروبي
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الانتقالات الفنية في تكوين اسلوب الفنان حافظ الدروبي

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Publication Date
Thu Oct 01 2020
Journal Name
مجلة العلوم الاقتصادية والإدارية
تقدير دالة البقاء بأستعمال الخوارزمية الجينية
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ان تحليل البقاء هو عبارة عن تحليل البيانات التي تكون في شكل اوقات من اصل الوقت حتى حدوث حدث النهاية ، وفي البحوث الطبية يكون اصل الوقت هو تاريخ تسجيل المفردة او المريض في دراسة ما مثل التجارب السريرية لمقارنة نوعين من الدواء او اكثر اذا كانت نقطة النهاية هي وفاة المريض او اختفاء المفردة فالبيانات الناتجة من هذه العملية تسمى اوقات البقاء اما اذا كانت النهاية هي ليست الوفاة فالبيانات الناتجة تسمى بيانات الوقت ح

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Publication Date
Tue Jun 01 2010
Journal Name
Journal Of Economics And Administrative Sciences
مقارنة طرائق تقدير معلمات دالة الشدة لعمليات بواسون غير المتجانسةمقارنة طرائق تقدير معلمات دالة الشدة لعمليات بواسون غير المتجانسةمقارنة طرائق تقدير معلمات دالة الشدة لعمليات بواسون غير المتجانسة
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This research deals with parameters estimation methods for the intensity function of non homogeneous poisson processes , it aims to estimate parameters of this function throughout three methods which are maximum likelihood method , moment method and shurnkage method using simulation method.

In order to achive the best method, several assumed values for parameters of intensity function have been adopted  using sample size of 
(14, 25, 50, 100) .Results of estimation showed that the estimation over the estimation method , of maximum likelihood and moment .

  This estimation gain the least mean of squares error for the above samples .

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