This study discussed a biased estimator of the Negative Binomial Regression model known as (Liu Estimator), This estimate was used to reduce variance and overcome the problem Multicollinearity between explanatory variables, Some estimates were used such as Ridge Regression and Maximum Likelihood Estimators, This research aims at the theoretical comparisons between the new estimator (Liu Estimator) and the estimators of Maximum Likelihood (ML) and Ridge Regression (RR) by using the mean square error (MSE) criterion, where the variance of the Maximum Likelihood (ML) comes in the presence of the problem Multicollinearity between the explanatory variables. In this study, the Monte Carlo simulation was designed to evaluate the performance of estimations using the criterion for comparison, the mean square error (MSE). The simulation results showed important an estimated Liu and superior to the RR and MLE estimator Where the number of explanatory variables is (p=5) and the sample size is (n=100), where the number of explanatory variables is (p=3) and for all sizes, and also when (p=5) for all sizes except size (n=100), the RR regression method is the best.
Estimation of mechanical and physical rock properties is an essential issue in applications related to reservoir geomechanics. Carbonate rocks have complex depositional environments and digenetic processes which alter the rock mechanical properties to varying degrees even at a small distance. This study has been conducted on seventeen core plug samples that have been taken from different formations of carbonate reservoirs in the Fauqi oil field (Jeribe, Khasib, and Mishrif formations). While the rock mechanical and petrophysical properties have been measured in the laboratory including the unconfined compressive strength, Young's modulus, bulk density, porosity, compressional and shear -waves, well logs have been used to do a compar
... Show MoreThis paper is submitted as anew approach to simulate manufacturing control & planning system to define the problem of designing control system on the needs for materials.
Production planning & control is a total and complex operation, resides in the essence of manufacturing companies operations. The successful process of production planning and control systems is critical for the staying of manufacturing organizations in markets leading to the increasing consumer competition and which dominate most of manufacturing sectors because of the market oriented economy , thus , what has happened previously , that the companies possessed a great inventory of crude material, components, and groupings and they use in flexible techni
... Show MoreThis article studies a comprehensive methods of edge detection and algorithms in digital images which is reflected a basic process in the field of image processing and analysis. The purpose of edge detection technique is discovering the borders that distinct diverse areas of an image, which donates to refining the understanding of the image contents and extracting structural information. The article starts by clarifying the idea of an edge and its importance in image analysis and studying the most noticeable edge detection methods utilized in this field, (e.g. Sobel, Prewitt, and Canny filters), besides other schemes based on distinguishing unexpected modifications in light intensity and color gradation. The research as well discuss
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This research aims to identify the impact of the Layout Ghazi al-Hariri hospital for surgery specialist on customer satisfaction (patients) using the model (Servicescape), the problem of the research represented in the extent to which the hospital management design of the service and Layout hospital aesthetic and functional aspects that fit patients for therapeutic and nursing services , and used the developer scale by (Miles et al., 2012) for data collection, which includes the independent variable in (17) items distributed in three dimensions (Facility aesthetics , hospital cleanliness, and the Layout accessibility ) The dependent variable is the satisfaction of customers (pat
... Show MoreThe present work aims to validate the experimental results of a new test rig built from scratch to evaluate the thermal behavior of the brake system with the numerical results of the transient thermal problem. The work was divided into two parts; in the first part, a three-dimensional finite-element solution of the transient thermal problem using a new developed 3D model of the brake system for the selected vehicle is SAIPA 131, while in the second part, the experimental test rig was built to achieve the necessary tests to find the temperature distribution during the braking process of the brake system. We obtained high agreement between the results of the new test rig with the numerical results based on the developed model of the brake
... Show MoreKurdish language multifunctional morphemes indicate the different functional morphological, syntactical, and semantic tasks of the morphemes. The present study discusses the multifunctional task of the Morpheme Le in Kurdish Language. The Morpheme Le has marginally been included in other studies, yet up to the present times, there has not been a research inclusively dedicated to thoroughly discuss and clarify its multifunctional aspects. The present study is divided into two chapters:
Chapter one discusses the theoretical aspect of chapter two that is entirely concerned with the practical aspect of the morpheme Le. The first part of chapte
... Show MoreThis research deals with the use of a number of statistical methods, such as the kernel method, watershed, histogram and cubic spline, to improve the contrast of digital images. The results obtained according to the RSME and NCC standards have proven that the spline method is the most accurate in the results compared to other statistical methods
The purpose of this paper is to model and forecast the white oil during the period (2012-2019) using volatility GARCH-class. After showing that squared returns of white oil have a significant long memory in the volatility, the return series based on fractional GARCH models are estimated and forecasted for the mean and volatility by quasi maximum likelihood QML as a traditional method. While the competition includes machine learning approaches using Support Vector Regression (SVR). Results showed that the best appropriate model among many other models to forecast the volatility, depending on the lowest value of Akaike information criterion and Schwartz information criterion, also the parameters must be significant. In addition, the residuals
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