The logistic regression model is an important statistical model showing the relationship between the binary variable and the explanatory variables.                                                        The large number of explanations that are usually used to illustrate the response led to the emergence of the problem of linear multiplicity between the explanatory variables that make estimating the parameters of the model not accurate.    

In this research, we use fuzzy nonparametric methods based on some smoothing techniques, were applied to real data on the Iraqi stock market especially the data about Baghdad company for soft drinks for the year (2016) for the period (1/1/2016-31/12/2016) .A sample of (148) observations was obtained in order to construct a model of the relationship between the stock prices (Low, high, modal) and the traded value by comparing the results of the criterion (G.O.F.) for three techniques , we note that the lowest value for this criterion was for the K-Nearest Neighbor at Gaussian function .

This study examined the effect of elements of internal marketing activities on customer satisfaction through an exploratory study of banking services in the city of Baghdad, if the research problem crystallized by asking a group of questions, the most important (is there a correlation and impact between the dimensions of internal marketing and customer satisfaction, this study aims to determine The effect of internal communication, training and internal market research on customer satisfaction, represented by the quality of service, loyalty and market share, and embodied the objective of this study to identify the level of availability of internal marketing activities and programs in the research banks, data were collected using

In this paper, the effect of changes in bank deposits on the money supply in Iraq was studied by estimating the error correction model (ECM) for monthly time series data for the period (2010-2015) . The Philips Perron was used to test the stationarity and also we used Engle and Granger to test the cointegration . we used cubic spline and local polynomial estimator to estimate regression function .The result show that local polynomial was better than cubic spline with the first level of cointegration.

Abstract:

      In this research we discussed the parameter estimation and variable selection in Tobit quantile regression model in present of multicollinearity problem. We used elastic net technique as an important technique for dealing with both multicollinearity and variable selection. Depending on the data we proposed Bayesian Tobit hierarchical model with four level prior distributions . We assumed both tuning parameter are random variable and estimated them with the other unknown parameter in the model .Simulation study was used for explain the efficiency of the proposed method and then we compared our approach with (Alhamzwi 2014 & standard QR) .The result illustrated that our approach

The research aims to shed light on banking liberalization and explain its impact on attracting customers, especially since Iraq adopted this policy after (2003) due to the changes that occurred, as the Central Bank of Iraq granted flexibility to banks in setting the interest rate on deposits and loans as well as allowing the entry of foreign banks in the local environment. The research relied on the analytical method for the dimensions of banking liberalization represented by (liberating interest rates, liberating credit, legal reserve requirements, entering foreign banks, privatization) as well as the factors affecting the attraction of customers, and a number of Iraqi banks listed in the Iraqi Stock Exchange were selected as a

In this research, the methods of Kernel estimator (nonparametric density estimator) were relied upon in estimating the two-response logistic regression, where the comparison was used between the method of Nadaraya-Watson and the method of Local Scoring algorithm, and optimal Smoothing parameter λ was estimated by the methods of Cross-validation and generalized Cross-validation, bandwidth optimal λ has a clear effect in the estimation process. It also has a key role in smoothing the curve as it approaches the real curve, and the goal of using the Kernel estimator is to modify the observations so that we can obtain estimators with characteristics close to the properties of real parameters, and based on medical data for patients with chro

The commercial job is one of the most important businesses in the cities as there is no city can exist without commercial markets to offer service to its inhabitants. This study aims to distribute the commercial function in Abu Tayara Street located in the municipality of Al-Dora by highlighting the size of such businesses in this street. It focuses on analyzing the change that has occurred due to the usages of the commercial land not only in the residential areas, but also in various aspects associated with the growth of the population in the city. Both expansion and development of the economic and social aspects in the area have increased the demand for commercial services. So, the research focuses on the aspects of functional speciali

            Semi-parametric regression models have been studied in a variety of applications and scientific fields due to their high flexibility in dealing with data that has problems, as they are characterized by the ease of interpretation of the parameter part while retaining the flexibility of the non-parametric part. The response variable or explanatory variables can have outliers, and the OLS approach have the sensitivity to outliers. To address this issue, robust (resistance) methods were used, which are less sensitive in the presence of outlier values in the data. This study aims to estimate the partial regression model using the robust estimation method with the wavel

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