Abstract

Characterized by the Ordinary Least Squares (OLS) on Maximum Likelihood for the greatest possible way that the exact moments are known , which means that it can be found, while the other method they are unknown, but approximations to their biases correct to 0(n^-1) can be obtained by standard methods. In our research expressions for approximations to the biases of the ML estimators (the regression coefficients and scale parameter) for linear (type 1) Extreme Value Regression Model for Largest Values are presented by using the advanced approach depends on finding the first derivative, second and third.

The aim of this paper is to approximate multidimensional functions f∈C(R^s) by developing a new type of Feedforward neural networks (FFNS) which we called it Greedy ridge function neural networks (GRGFNNS). Also, we introduce a modification to the greedy algorithm which is used to train the greedy ridge function neural networks. An error bound are introduced in Sobolov space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Backpropagation algorithm and the result in [1]).

As the process of  estimate for model and variable selection significant is a crucial process in the semi-parametric modeling At the beginning of the modeling process often At there are many explanatory variables to Avoid the loss of any explanatory elements may be important as a result , the selection of significant variables become necessary , so the process of variable selection is not intended to simplifying  model complexity explanation , and also predicting. In this research was to use some of the semi-parametric methods (LASSO-MAVE , MAVE and The proposal method (Adaptive LASSO-MAVE) for variable selection and estimate semi-parametric single index model (SSIM) at the same time .

In this paper, we will study non parametric model when the response variable have missing data (non response) in observations it under missing mechanisms MCAR, then we suggest Kernel-Based Non-Parametric Single-Imputation instead of missing value and compare it with Nearest Neighbor Imputation by using the simulation about some difference models and with difference cases as the sample size, variance and rate of missing data.      

 Is the subject of the financial structure of the most important topics for which she received the interests of scientific research in the field of financial management , as it emerged several theories about choosing a financial structure appropriate for the facility and behavior change funding them , and in spite of that there is no agreement on a specific theory answer various questions in this regard , and a special issue of the financial structure optimization.

The objective of the research was to identify the most important theories of the structure of modern financial theory has been to focus on the capture of financial firms in two different stages of their life cycle , so-called growth and ma

This research aims primarily to highlight personal tax exemptions A comparative study with some Arab and European regulations. And by conducting both theoretical comparative analyses. Most important findings of the study is the need to grant personal and family exemptions that differ according to the civil status of the taxpayer (single or married). In other words, the exemption increases as the number of family members depend on its social sense. Also taking into account some incomes that require a certain effort and looking at the tax rates, it is unreasonable for wages to be subject to the same rates applied to commercial profits.      

An adaptive fuzzy weighted linear regression model in which the output is based
on the position and entropy of quadruple fuzzy numbers had dealt with. The solution
of the adaptive models is established in terms of the iterative fuzzy least squares by
introducing a new suitable metric which takes into account the types of the influence
of different imprecisions. Furthermore, the applicability of the model is made by
attempting to estimate the fuzzy infant mortality rate in Iraq using a selective set of
inputs.

Abstract

  Bivariate time series modeling and forecasting have become a promising field of applied studies in recent times. For this purpose, the Linear Autoregressive Moving Average with exogenous variable ARMAX model is the most widely used technique over the past few years in modeling and forecasting this type of data. The most important assumptions of this model are linearity and homogenous for random error variance of the appropriate model. In practice, these two assumptions are often violated, so the Generalized Autoregressive Conditional Heteroscedasticity (ARCH) and (GARCH) with exogenous varia

تعد لوحات السيطرة الخاصة بالمراقبة والسيطرة على نوعية الانتاج احدى الاساليب العلمية الاحصائية التي تستخدم لمراقبة سير العملية الانتاجية اثناء سيرالعنلية الانتاجية اثناء سيرها في مراحل الانتاج والتي عادة ما تتكون من حد وسطي وحدين اعلى وادنى للسيطرة على نوعية ودقة الانتاج متمثلا بقيم عددية . ومن ثم فان العملية الانتاجية اما ان تكون تحت السيطرة او خارجها بالاعتماد على قيم المشاهادات العددية. وفي بعض الاحيا