The idea of ​​carrying out research on incomplete data came from the circumstances of our dear country and the horrors of war, which resulted in the missing of many important data and in all aspects of economic, natural, health, scientific life, etc.,. The reasons for the missing are different, including what is outside the will of the concerned or be the will of the concerned, which is planned for that because of the cost or risk or because of the lack of possibilities for inspection. The missing data in this study were processed using Principal Component  Analysis and self-organizing map methods using simulation. The variables of child health and variables affecting children's health were taken into account: breastfeed

   In this paper , two method which deal with finding the optimal value for adaptive smoothing constant, are compared .This constant is used in adaptive Single Exponential Smoothing (ASES).

The comparing is between a method uses time domain and another uses frequency domain when the data contain outlier value for autoregressive model of order one AR(1) , or Markov Model, when the time series are stationary and non stationary with deferent samples .    

In this research we study a variance component model, Which is the one of the most important models widely used in the analysis of the data, this model is one type of a multilevel models, and it is considered as linear models , there are three types of linear variance component models ,Fixed effect of linear variance component model, Random effect of linear variance component model and Mixed effect of linear variance component model . In this paper we will examine the model of mixed effect of linear variance component model with one –way random effect ,and the mixed model is a mixture of fixed effect and random effect in the same model, where it contains the parameter (μ) and treatment effect (τ_i ) which  has

Journal of Studies and Researches of Sport Education

المقدمة

بفضل التقدم الذي حصل في الحاسبات في العقد الأخير والذي انعكست آثاره على البرامج الجاهزة اعتبرت المشكلة التي في حدود بضعة آلاف من المتغيرات والقيود من المشاكل الصغيرة إذ تم حل مشاكل بعشرات الآلاف أو مئات الآلاف من المتغيرات المستمرة حلا مناسبا.وبفضل التقدم التكنولوجي للحاسبات وسرعة معاملتها للبيانات والمعادلات تم بناء برامج جاهزة لأساليب بحوث العمليات ومعالجة المشاكل الإدار

Nonlinear time series analysis is one of the most complex problems ; especially the nonlinear autoregressive with exogenous variable (NARX) .Then ; the problem of model identification and the correct orders determination considered the most important problem in the analysis of time series . In this paper , we proposed splines  estimation method for model identification , then we used three criterions for the correct orders determination. Where ; proposed method used to estimate the additive splines for model identification , And the rank determination depends on the additive property  to avoid the problem of curse dimensionally . The proposed method is one of the nonparametric methods , and the simulation results give a

هدف البحث الى إعداد تمرينات تعليمية بمصاحبة بعض الوسائط التفاعلية في بعض القدرات العقلية والاداء الفني لركض 100 حواجز طالبات، وكانت عينة البحث هم طالبات كلية التربية البدنية وعلوم الرياضة للبنات / جامعه بغداد المرحلة الثانية وكان عددهم (20) طالبة واستعملت الباحثة المنهج التجريبي واستعملت تمرينات تعليمية بمصاحبة بعض الوسائط التفاعلية للمجموعة التجريبية لمدة (6 أسابيع) بواقع وحدتين تعليمية في الأسبوع واستن

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

In this study, we made a comparison between LASSO & SCAD methods, which are two special methods for dealing with models in partial quantile regression. (Nadaraya & Watson Kernel) was used to estimate the non-parametric part ;in addition, the rule of thumb method was used to estimate the smoothing bandwidth (h). Penalty methods proved to be efficient in estimating the regression coefficients, but the SCAD method according to the mean squared error criterion (MSE) was the best after estimating the missing data using the mean imputation method