A mixture model is used to model data that come from more than one component. In recent years, it became an effective tool in drawing inferences about the complex data that we might come across in real life. Moreover, it can represent a tremendous confirmatory tool in classification observations based on similarities amongst them. In this paper, several mixture regression-based methods were conducted under the assumption that the data come from a finite number of components. A comparison of these methods has been made according to their results in estimating component parameters. Also, observation membership has been inferred and assessed for these methods. The results showed that the flexible mixture model outperformed the others in most simulation scenarios according to the integrated mean square error and integrated classification error
Abstract
The research to have a clear perceptions about the knowledge value added to assess the knowledge resources of the Iraqi private banks, depending on the value added methodology of the proposed defined (Housel & Bell, 2001), which assumes that the knowledge value added come through synergetic relationship between knowledge resource and information technology, trying to the possibility of mainstream theory and its application in the Iraqi environment and interpretation of results, and on this basis was launched search of a research problem took root synergetic nature of the relationship between knowledge (human) resource and
... Show MoreIn this paper, a new analytical method is introduced to find the general solution of linear partial differential equations. In this method, each Laplace transform (LT) and Sumudu transform (ST) is used independently along with canonical coordinates. The strength of this method is that it is easy to implement and does not require initial conditions.
The research has been concerned with the modalities of foreign trade payments (foreign trade financing), and made an accounting comparison between them to choose the best way to pay for the imported goods (payment of the real values of imported goods), given the importance of the impact of this activity on the national economy of all countries of the world, especially Iraq for the adoption of a very large amount of imported goods to meet the requirements of the people, which require the flow of huge amounts of foreign currency outside Iraq to pay for these goods, and therefore dealing incorrectly with it leads to the destruction of the national economy and the spread of a number of negative social and economic phenomena of
... Show Moreيتضمن البحث دراسة لزوجة محاليل تحتوي على املاح كلوريد البوتاسيوم وبروميد البوتاسيوم في مزيج من الماء وداي مثيل سلفوكسايد 60% وزنا داي مثيل سلفوكسايد.وقد اجريت الدراسة بست درجات حرارية مختلفة ونوقشت امكانية في ضوء معادلة جونز- دول حيث اخذ بنظر الاعتبار الحجم الايوني والشحنة وشكل جزيئات المذاب.
Abstract:
Since the railway transport sector is very important in many countries of the world, we have tried through this research to study the production function of this sector and to indicate the level of productivity under which it operates.
It was found through the estimation and analysis of the production function Kub - Duglas that the railway transport sector in Iraq suffers from a decline in the level of productivity, which was reflected in the deterioration of the level of services provided for the transport of passengers and goods. This led to the loss of the sector of importance in supporting the national economy and the reluctance of most passengers an
... Show MoreTraumatic spinal cord injury is a serious neurological disorder. Patients experience a plethora of symptoms that can be attributed to the nerve fiber tracts that are compromised. This includes limb weakness, sensory impairment, and truncal instability, as well as a variety of autonomic abnormalities. This article will discuss how machine learning classification can be used to characterize the initial impairment and subsequent recovery of electromyography signals in an non-human primate model of traumatic spinal cord injury. The ultimate objective is to identify potential treatments for traumatic spinal cord injury. This work focuses specifically on finding a suitable classifier that differentiates between two distinct experimental
... Show MoreThe two most popular models inwell-known count regression models are Poisson and negative binomial regression models. Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes the response variable Y has a Poisson distribution, and assumes the logarithm of its expected value can be modeled by a linear combination of unknown parameters. Negative binomial regression is similar to regular multiple regression except that the dependent (Y) variables an observed count that follows the negative binomial distribution. This research studies some factors affecting divorce using Poisson and negative binomial regression models. The factors are unemplo
... Show MoreTransforming the common normal distribution through the generated Kummer Beta model to the Kummer Beta Generalized Normal Distribution (KBGND) had been achieved. Then, estimating the distribution parameters and hazard function using the MLE method, and improving these estimations by employing the genetic algorithm. Simulation is used by assuming a number of models and different sample sizes. The main finding was that the common maximum likelihood (MLE) method is the best in estimating the parameters of the Kummer Beta Generalized Normal Distribution (KBGND) compared to the common maximum likelihood according to Mean Squares Error (MSE) and Mean squares Error Integral (IMSE) criteria in estimating the hazard function. While the pr
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