زاد اهتمام الباحثين في السنوات الاخيرة بنماذج الانحدار شبه المعلمية وذلك اذ يمكن من خلالها دمج نماذج الانحدار اللامعلمي والمعلمي في ان واحد ومن ثم تكوين انموذج انحدار يتميز بامكانية التعامل مع مشكلة تعدد الابعاد الموجودة في النماذج اللامعلمية والتي تحصل عند زيادة عدد المتغيرات التوضيحية الداخلة في التحليل و من ثم تناقص دقة التقدير.  فضلا عن امتياز هذا النوع من النماذج بمرونة في المجال التطبيقي مقارنةً

من اهداف بعض التجارب هي معرفة تاثير التسلسلات المختلفة لبعض الادوية او التغذية او تجارب التعلم. وفي بعض الاحيان قد تكون الوحدات التجريبية نادرة لهذا نقوم باستخدام الوحدات التجريبية على نحو متكرر. او بسبب الميزانية المحدودة فان صاحب التجربة يخضع كل وحدة تجريبية لاختبارات عديدة ويطلق على هذا النوع من التجارب التي يتم فيها استخدام الوحدات التجريبية (الاشخاص) Subject على نحو متكرر

The question of estimation took a great interest in some engineering, statistical applications, various applied, human sciences, the methods provided by it helped to identify and accurately the many random processes.

In this paper, methods were used through which the reliability function, risk function, and estimation of the distribution parameters were used, and the methods are (Moment Method, Maximum Likelihood Method), where an experimental study was conducted using a simulation method for the purpose of comparing the methods to show which of these methods are competent in practical application This is based on the observations generated from the Rayleigh logarithmic distribution (RL) with sample sizes

            In this paper, the process of comparison between the tree regression model and the negative binomial regression. As these models included two types of statistical methods represented by the first type "non parameter statistic" which is the tree regression that aims to divide the data set into subgroups, and the second type is the "parameter statistic" of negative binomial regression, which is usually used when dealing with medical data, especially when dealing with large sample sizes. Comparison of these methods according to the average mean squares error (MSE) and using the simulation of the experiment and taking different sample

Abstract

The Classical Normal Linear Regression Model Based on Several hypotheses, one of them is Heteroscedasticity as it is known that the wing of least squares method (OLS), under the existence of these two problems make the estimators, lose their desirable properties, in addition the statistical inference becomes unaccepted table. According that we put tow alternative,  the first one is  (Generalized Least Square) Which is denoted by (GLS), and the second alternative is to (Robust covariance matrix estimation) the estimated parameters method(OLS), and that the way (GLS) method neat and certified, if the capabilities (Efficient) and the statistical inference Thread on the basis of an acceptable

This study is about finding the estimation of tow equations, the comparative has been done between the estimations  by using seemingly unrelated regression equations for the variable and random error has been distribution with poisson and the variable and random error has been distribution with normal and the method by using oldenary lest square.

While in the application side, we have estimated the parameter of investment specification function for the sector of agriculture with the industry sector is enabled us to obtain an estimation efficiency for the model of seemingly unrelated Poisson regression equation.

المستخلص

   A comparison of double informative and non- informative priors assumed for the parameter of Rayleigh distribution is considered. Three different sets of double priors are included, for a single unknown parameter of Rayleigh distribution. We have assumed three double priors: the square root inverted gamma (SRIG) - the natural conjugate family of priors distribution, the square root inverted gamma – the non-informative distribution, and the natural conjugate family of priors - the non-informative distribution as double priors .The data is generating form three cases from Rayleigh distribution for different samples sizes (small, medium, and large). And Bayes estimators for the parameter is derived under a squared erro

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 obse

The recent development in statistics has made statistical distributions the focus of researchers in the process of compensating for some distribution parameters with fixed values and obtaining a new distribution, in this study, the distribution of Kumaraswamy was studied from the constant distributions of the two parameters. The characteristics of the distribution were discussed through the presentation of the probability density function (p.d.f), the cumulative distribution function (c.d.f.), the ratio of r, the reliability function and the hazard function. The parameters of the Kumaraswamy distribution were estimated using MLE, ME, LSEE by using the simulation method for different sampling sizes and using preli