This study relates to  the estimation of  a simultaneous equations system for the Tobit model where the dependent variables  ( )  are limited, and this will affect the method to choose the good estimator. So, we will use new estimations methods  different from the classical methods, which if used in such a case, will produce biased and inconsistent estimators which is (Nelson-Olson) method  and  Two- Stage limited dependent variables(2SLDV) method  to get of estimators that hold characteristics the good estimator .

That is , parameters will be estim

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

This article aims to estimate the partially linear model by using two methods, which are the Wavelet and Kernel Smoothers. Simulation experiments are used to study the small sample behavior depending on different functions, sample sizes, and variances. Results explained that the wavelet smoother is the best depending on the mean average squares error criterion for all cases that used.

Survival analysis is widely applied to data that described by the length of time until the occurrence of an event under interest such as death or other important events. The purpose of this paper is to use the dynamic methodology which provides a flexible method, especially in the analysis of discrete survival time, to estimate the effect of covariate variables through time in the survival analysis on dialysis patients with kidney failure until death occurs. Where the estimations process is completely based on the Bayes approach by using two estimation methods: the maximum A Posterior (MAP) involved with Iteratively Weighted Kalman Filter Smoothing (IWKFS) and in combination with the Expectation Maximization (EM) algorithm. While the other

Often times, especially in practical applications, it is difficult to obtain data that is not tainted by a problem that may be related to the inconsistency of the variance of error or any other problem that impedes the use of the usual methods represented by the method of the ordinary least squares (OLS), To find the capabilities of the features of the multiple linear models, This is why many statisticians resort to the use of estimates by immune methods Especially with the presence of outliers, as well as the problem of error Variance instability, Two methods of horsepower were adopted, they are the robust weighted least square(RWLS)& the two-step robust weighted least square method(TSRWLS), and their performance was verifie

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

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