In this paper, the error distribution function is estimated for the single index model by the empirical distribution function and the kernel distribution function. Refined minimum average variance estimation (RMAVE) method is used for estimating single index model. We use simulation experiments to compare the two estimation methods for error distribution function with different sample sizes, the results show that the kernel distribution function is better than the empirical distribution function.

The Dagum Regression Model, introduced to address limitations in traditional econometric models, provides enhanced flexibility for analyzing data characterized by heavy tails and asymmetry, which is common in income and wealth distributions. This paper develops and applies the Dagum model, demonstrating its advantages over other distributions such as the Log-Normal and Gamma distributions. The model's parameters are estimated using Maximum Likelihood Estimation (MLE) and the Method of Moments (MoM). A simulation study evaluates both methods' performance across various sample sizes, showing that MoM tends to offer more robust and precise estimates, particularly in small samples. These findings provide valuable insights into the ana

Generalized Additive Model has been considered as a multivariate smoother that appeared recently in Nonparametric Regression Analysis. Thus, this research is devoted to study the mixed situation, i.e. for the phenomena that changes its behaviour from linear (with known functional form) represented in parametric part, to nonlinear (with unknown functional form: here, smoothing spline) represented in nonparametric part of the model. Furthermore, we propose robust semiparametric GAM estimator, which compared with two other existed techniques.

      The main problem when dealing with fuzzy data variables is that it cannot be formed by a model that represents the data through the method of Fuzzy Least Squares Estimator (FLSE) which gives false estimates of the invalidity of the method in the case of the existence of the problem of multicollinearity. To overcome this problem, the Fuzzy Bridge Regression Estimator (FBRE) Method was relied upon to estimate a fuzzy linear regression model by triangular fuzzy numbers. Moreover, the detection of the problem of multicollinearity in the fuzzy data can be done by using Variance Inflation Factor when the inputs variable of the model crisp, output variable, and parameters are fuzzed. The results were compared usin

      This work addressed the assignment problem (AP) based on fuzzy costs, where the objective, in this study, is to minimize the cost. A triangular, or trapezoidal, fuzzy numbers were assigned for each fuzzy cost. In addition, the assignment models were applied on linguistic variables which were initially converted to quantitative fuzzy data by using the Yager’sorankingi method. The paper results have showed that the quantitative date have a considerable effect when considered in fuzzy-mathematic models.

In Iraq 1.4 million of people have diabetes, the prevalence of T2DM was ranged (8.5%—13.9%), and the cluster of metabolic abnormalities has long been identified as the risk factors for type 2 diabetes and is now commonly described as metabolic syndrome/MetS. Insulin resistance takes a key role in the process of the MetS and has even been hypothesized as its underlying cause. Clinical and epidemiologic studies also indicate that obesity and life style habit might be correlated with IR. This study examined the relationship between IR and MetS in a sample of young, healthy university students in Iraq. It discovered that the severity of IR is positively correlated with the clustering of MetS risk factors in Iraqi students, suggesting

There are two ways that the contract might be formed with (contracting between persons who are attended and contracting between absence persons).the need for determining the precise moment of the contract , is so clear because there is a specify period separate between the declaration of acceptance and the knowledge with it .and it is clear from the four theories known for jurisprudence (theory of the declaration of the acceptance, theory of exporting the acceptance , theory of the arrival of the acceptance , theory of the knowledge with the acceptance ) . It is difficult to promote one theory on another one if we look at each one and the justification of its supporters and what the opponents of each theory expose. Legal background and diff