The estimation of the parameters of Two Parameters Gamma Distribution in case of missing data has been made by using two important methods: the Maximum Likelihood Method and the Shrinkage Method. The former one consists of three methods to solve the MLE non-linear equation by which the estimators of the maximum likelihood can be obtained: Newton-Raphson, Thom and Sinha methods. Thom and Sinha methods are developed by the researcher to be suitable in case of missing data. Furthermore, the Bowman, Shenton and Lam Method, which depends on the Three Parameters Gamma Distribution to get the maximum likelihood estimators, has been developed. A comparison has been made between the methods in the experimental aspect to find the best meth

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 techniq

In linear regression, an outlier is an observation with large residual.  In other words, it is an observation whose dependent-variable value is unusual given its values on the predictor variables. An outlier observation may indicate a data entry error or other problem.

An observation with an extreme value on a predictor variable is a point with high leverage. Leverage is a measure of how far an independent variable deviates from its mean. These leverage points can have an effect on the estimate of regression coefficients.

Robust estimation for regression parameters deals with cases that have very high leverage, and cases that are outliers. Robust estimation is essentially a

 يدرس هذا البحث طرائق اختزال الابعاد التي تعمل على تجاوز مشكلة البعدية عندما تفشل الطرائق التقليدية في ايجاد تقدير جيد للمعلمات، لذلك يتوجب التعامل مع هذه المشكلة بشكل مباشر. ومن اجل ذلك، يجب التخلص من هذه المشكلة لذا تم استعمال اسلوبين لحل مشكلة البيانات ذات الابعاد العالية الاسلوب الاول طريقة الانحدار الشرائحي المعكوس SIR ) ) والتي تعتبر طريقة غير كلاسيكية  وكذلك طريقة ( WSIR ) المقترحة والاسلوب الثاني طري

In this paper, the generalized inverted exponential distribution is considered as one of the most important distributions in studying failure times. A shape and scale parameters of the distribution have been estimated after removing the fuzziness that characterizes its data because they are triangular fuzzy numbers. To convert the fuzzy data to crisp data the researcher has used the centroid method. Hence the studied distribution has two parameters which show a difficulty in separating and estimating them directly of the MLE method. The Newton-Raphson method has been used.

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

في كثير من الأحيان يفشل تحليل المربعات الصغرى (LS) تماماً في حالة وجود قيم شاذة في الظواهر المدروسة، اذ ستفقد OLS خصائصها ومن ثم تفقد صفة المقدر الخطي الجيد Beast Linear Unbiased Estimator (BLUE) لِما تسببه الشواذ Outliers من تأثير سيئ علـى نتـائج التحليـل الاحـصائي للبيانـات اذ أن وجودها يؤدي الى إرباك كبير في تحليل البيانات في حالة إستخدام الطرائق التقليدية، ولعلاج هذه المشكلة تم تطوير أساليب إحصائية جديدة بحيث لا تتأثر بالقي

         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

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

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