In this paper we suggest new method to estimate the missing data  in bivariate normal distribution and compare it with Single Imputation  method (Unconditional mean and Conditional mean) by using simulation. 

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

  Estimation stage is one of most important in process of selecting and identification for fit model, this model gives a best results if the good methods of estimation are depended on, one of those methods is Bayes method for estimation the parameters, it puts an assumption that parameter have a distribution.

 This paper studies the robustness of estimators of empirical Bayes to know the properties of those estimators.

سنقوم في هذا البحث باشتقاق توزيع الطلب خلال فترة الانتظار لنظام سيطرة على الخزين يخضع فيه الطلب لتوزيع گاما فيما يخضع وقت الانتظار للتوزيع اللوغايتمي الطبيعي، كما سيتم استخراج العزوم الأساسية لهذا المتغير ، الضرورية بدورها لاستخراج بعض مؤشرات النظام المذكور.

المصطلحات المستخدمة: التكامل المحيط، المستوي المركب، تكامل هانكيل، مستوى إعادة الطلب، الوقاية.

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

The principal components analysis is used in analyzing many economic and social phenomena; and one of them is related to a large group in our society who are the university instructors. This phenomenon is the delay occurred in getting university instructor to his next scientific title. And as the determination of the principal components number inside the principal components depends on using many methods, we have compared between three of these methods that are: (BARTLETT, SCREE DIAGRAM, JOLLIFFE).

     We concluded that JOLLIFFE method was the best one in analyzing the studying phenomenon data among these three methods, we found the most distinguishing factors effecting on t

The analysis of time series considers one of the mathematical and statistical methods in explanation of the nature phenomena and its manner in a specific time period.

Because the studying of time series can get by building, analysis the models and then forecasting gives the priority for the practicing in different fields, therefore the identification and selection of the model is of great importance in spite of its difficulties.

The selection of a standard methods has the ability for estimation the errors in the estimated the parameters for the model, and there will be a balance between the suitability and the simplicity of the model.

In the analysis of d

The repeated measurement design is called a complete randomized block design for repeated measurement when the subject is given the all different treatments , in this case the subject is considered as a block . Many of nonparametric methods were considered like Friedman test (1937) and Koch test(1969) and Kepner&Robinson test(1988) when the assumption of normal distribution of the data is not satisfied .as well as F test  when the assumptions of the analysis of variance is satisfied ,where the observations within blocks are assumed to be equally correlated . The purpose of this paper is to summarize the result of the simulation study for comparing these methods as well as present the suggested

Me

In this research weights, which are used, are estimated using General Least Square Estimation to estimate simple linear regression parameters when the depended variable, which is used, consists of two classes attributes variable (for Heteroscedastic problem) depending on Sequential Bayesian Approach instead of the Classical approach used before, Bayes approach provides the mechanism of tackling observations one by one in a sequential way, i .e each new observation will add a new piece of information for estimating the parameter of probability estimation of certain phenomenon of Bernoulli trials who research the depended variable in simple regression  linear equation. in addition to the information deduced from the past exper

In this paper, compared eight methods for generating the initial value and the impact of these methods to estimate the parameter of a autoregressive model, as was the use of three of the most popular methods to estimate the model and the most commonly used by researchers MLL method, Barg method  and the least squares method and that using the method of simulation model  first order autoregressive through the design of a number of simulation experiments and the different sizes of the samples.