يعد أنموذج الانحدار اللوجستي من نماذج الانحدار المهمة، حيث يلقى اهتماماً واضحاً في معظم الدراسات التي تأخذ طابعاً اكثر تقدماً في عملية التحليل الاحصائي. أن طرائق التقدير الاعتيادية تفشل في التعامل مع البيانات التي تتضمن وجود القيم الشاذة حيث أن لها تأثير غير مرغوب على النتائج. سنستعرض في هذا البحث طرائق لتقدير معلمات انموذج الانحدار اللوجستي وهذه الطرائق هي: طريقة مقدر لابلاس (Laplace estimator) (LP-) وطريقة مقدر هوب

         The availability of statistical data plays an important role in planning process. The importance of this research which deals with safety of statistical data from errors and outliers values. The Objective of this study is to determine the outlier values in statistical data by using modern exploratory data methods and comparing them with parametric methods. The research has been divided into four chapters ,the main important conclusions reached are:1-The exploratory methods and the parametric methods showed variation between them in determining the outlier values in the data.

2-The study showed that the box plot method was the best method used in determining

The logistic regression model regarded as the important regression Models ,where of the most interesting subjects in recent studies due to taking character more advanced in the process of statistical analysis .                                                

The ordinary estimating methods is failed in dealing with data that consist of the presence of outlier values and hence on the absence of such that have undesirable effect on the result.    &nbs

Kinetic analysis has received great importance in the fields of sports and biomedicine, as it provides accurate data about the motor performance of athletes and helps in improving performance and preventing injuries, and among the technological tools currently available, artificial intelligence applications such as the (on form) application, which works to analyze performance directly and indirectly and has several advantages where direct analysis of performance is possible and reduce time and costs without referring to the video and analyzing it with analysis programs such as the (kenovea) program, which needs more time and greater experience by the person analyzing it, The research aimed at a comparative study to measure some mechanical v

Nonparametric methods are used in the data that contain outliers values. The main importance in using Nonparametric methods is to locate the median in the multivariate regression model. It is difficult to locate the median due to the presence of more than one dimension and the dispersion of values and the increase of the studied phenomenon data.The genetic algorithms Minimum Weighted Covariance Determinant Estimator (MWCD), was applied and compared with the multilayer neural network Back propagation to find the estimate of the median location based on the minimum distance (Mahalanobis Distance) and smallest specified for the variance matrix. Joint Minimum Covariance Determinant (MCD) as one of the most nonparametric methods robust. The stud

The goal beyond this Research is to review methods that used to estimate Logistic distribution parameters. An exact estimators method which is the Moment method, compared with other approximate estimators obtained essentially from White approach such as: OLS, Ridge, and Adjusted Ridge as a suggested one to be applied with this distribution. The Results of all those methods are based on Simulation experiment, with different models and variety of  sample sizes. The comparison had been made with respect to two criteria: Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE).  

The development in manufacturing computers from both (Hardware and Software) sides, make complicated robust estimators became computable and gave us new way of dealing with the data, when classical discriminant methods failed in achieving its optimal properties especially when data contains a percentage of outliers. Thus, the inability to have the minimum probability of misclassification. The research aim to compare robust estimators which are resistant to outlier influence like robust H estimator, robust S estimator and robust MCD estimator, also robustify misclassification probability with showing outlier influence on the percentage of misclassification when using classical methods. ,the other

Mixed-effects conditional logistic regression is evidently more effective in the study of qualitative differences in longitudinal pollution data as well as their implications on heterogeneous subgroups. This study seeks that conditional logistic regression is a robust evaluation method for environmental studies, thru the analysis of environment pollution as a function of oil production and environmental factors. Consequently, it has been established theoretically that the primary objective of model selection in this research is to identify the candidate model that is optimal for the conditional design. The candidate model should achieve generalizability, goodness-of-fit, parsimony and establish equilibrium between bias and variab

    This research aims to review the importance of estimating the nonparametric regression function using so-called Canonical Kernel which depends on re-scale the smoothing parameter, which has a large and important role in Kernel  and give the sound amount of smoothing .

We has been shown the importance of this method through the application of these concepts on real data refer to international exchange rates to the U.S. dollar against the Japanese yen for the period from January 2007 to March 2010. The results demonstrated preference the nonparametric estimator with Gaussian on the other nonparametric and parametric regression estima

تركت العولمة المتسارعة أثرا عميقا على مدى واسع من السياسات والتطبيقات الاقتصادية في كل من الدول النامية والمتقدمة على حد سواء, إذ نشأت الأنظمة الضريبية الحالية عندما نمّط كل بلد سياسته الضريبية بشكل تركز معه على متطلبات الاقتصاد المحلي, وعندما كانت تعقد المفاوضات بين الأمم المختلفة حول المعاملات الضريبية والاتفاقات الضريبية كانت تجري في إطار من هيمنة السياسة الضريبية المحلية.

لكن العولم