The aim of this essay is to use a single-index model in developing and adjusting Fama-MacBeth.  Penalized smoothing spline regression technique (SIMPLS) foresaw this adjustment.  Two generalized cross-validation techniques, Generalized Cross Validation Grid (GGCV) and Generalized Cross Validation Fast (FGCV), anticipated the regular value of smoothing covered under this technique. Due to the two-steps nature of the Fama-MacBeth model, this estimation generated four estimates: SIMPLS(FGCV) - SIMPLS(FGCV), SIMPLS(FGCV) - SIM PLS(GGCV), SIMPLS(GGCV) - SIMPLS(FGCV), SIM PLS(GGCV) - SIM PLS(GGCV). Three-factor Fama-French model—market risk premium, size factor, value factor, and their implication for excess stock returns and portfolio return

In this paper, Bayes estimators for the shape and scale parameters of Gamma distribution under the Entropy loss function have been obtained, assuming Gamma and Exponential priors for the shape and scale parameters respectively. Moment, Maximum likelihood estimators and Lindley’s approximation have been used effectively in Bayesian estimation. Based on Monte Carlo simulation method, those estimators are compared depending on the mean squared errors (MSE’s). The results show that, the performance of the Bayes estimator under Entropy loss function is better than other estimates in all cases.   

النموذج التجميعي المعمّم GAM من الممهدات متعددة المتغيرات حديثة الأستعمال في تحليل الأنحدار اللامعلمي، ولذا تم تكريس هذا البحث لدراسته ولكن بالصيغة الهجينة،أي عند للظاهرة التي تغير سلوكها من خطي ذات شكل دالي مسبق (معلمي)، ولاخطي بشكل غير معلوم (لامعلمي) والذي سيكون هنا الشريحة التمهيدية. هذا، بالأضافة الى تقديم مقترح لأيجاد مقدّر GAM شبه معلمي حصين ومقارنته بأسلوب تكراري وآخر غير تكراري.

In this research, one of the nonlinear regression models is studied, which is BoxBOD, which is characterized by nonlinear parameters, as the difficulty of this model lies in estimating its parameters for being nonlinear, as its parameters were estimated by some traditional methods, namely the method of non-linear least squares and the greatest possible method and one of the methods of artificial intelligence, it is a genetic algorithm, as this algorithm was based on two types of functions, one of which is the function of the sum of squares of error and the second is the function of possibility. For comparison between the methods used in the research, the comparison scale was based on the average error squares, and for the purpose of data ge

This Book is intended to be textbook studied for undergraduate course in multivariate analysis. This book is designed to be used in semester system. In order to achieve the goals of the book, it is divided into the following chapters. Chapter One introduces matrix algebra. Chapter Two devotes to Linear Equation System Solution with quadratic forms, Characteristic roots & vectors. Chapter Three discusses Partitioned Matrices and how to get Inverse, Jacobi and Hessian matrices. Chapter Four deals with Multivariate Normal Distribution (MVN). Chapter Five concern with Joint, Marginal and Conditional Normal Distribution, independency and correlations. Many solved examples are intended in this book, in addition to a variety of unsolved relied pro

يعتبر التلوث احد المشكلات المهمة التي تواجه البشرية في الوقت الحاضر نتيجة للنشاط الإنساني المتزايد في مجالات الحياة المختلفة وتوجد أنواع مختلفة من اشكال التلوث، ويعتبر تلوث الهواء احد مظاهر التلوث خطورة، لذلك اجريت بحثا باستعمال انموذج الانحدار اللوجستي حيث يعتبر من النماذج الكفؤة والملائمة في عملية تحليل البيانات الوصفية ثنائية الاستجابة، وانموذج بروبت الذي يشبه أنموذج اللوجستي الثنائي في طبيعة ا

يعد أنموذج الانحدار اللوجستي من نماذج الانحدار المهمة، حيث يلقى اهتماماً واضحاً في معظم الدراسات التي تأخذ طابعاً اكثر تقدماً في عملية التحليل الاحصائي. أن طرائق التقدير الاعتيادية تفشل في التعامل مع البيانات التي تتضمن وجود القيم الشاذة حيث أن لها تأثير غير مرغوب على النتائج. سنستعرض في هذا البحث طرائق لتقدير معلمات انموذج الانحدار اللوجستي وهذه الطرائق هي: طريقة مقدر لابلاس (Laplace estimator) (LP-) وطريقة مقدر هوب

The educational sector is one of the important sectors in the world, and it is considered one of the means of community development. In addition, it is one of the means of making the country’s renaissance and devel-opment because it represents the factory of thinking minds that make change. There is no doubt that this sector is the same as any other sector. The deficit in the studied scientific planning has been prolonged, which led to its deterioration, and the problems of education remain diverse and inherited from previous time periods, where the hierarchical cluster analysis was used on postgraduate students in universities in Iraq, except for Kurdistan region, and the number of universities that were included in the study was

 في هذا البحث سيتم دراسة أنموذج الانحدار اللامعلمي الذي يعاني فيه متغير الأستجابة من حالة فقدان (عدم استجابة) في بعض مشاهداتة وتحت أفتراض الية فقدان MCAR، إذ تم اقتراح طريقة تعويض قاعدة Kernel الأحادي اللامعلمي بدلاً عن القيمة المفقودة ومقارنة هذه الطريقة مع طريقة تعويض أقرب مجاور بأستخدام أسلوب المحاكاة والمتمثل بعدة تجارب لعدة نماذج مختلفة ولحالات مختلفة من حجوم العينة، التباين ونسب الفقدان.             <

في هذا البحث تم تقديم عدد من المقدرات الخاصة بتقدير معلمة عرض الصندوق لواحد من اكثر مقدرات دالة الكثافة الاحتمالية شيوعا وهو مايسمى بالمدرج التكراري، وقد تم استخدام اسلوب المحاكاة لمقارنة تلك المقدرات اذ اثبتت النتائج افضلية اسلوب قاعدة الابهام لاكثر التجارب المقامة.

In the lifetime process in some systems, most data cannot belong to one single population. In fact, it can represent several subpopulations. In such a case, the known distribution cannot be used to model data. Instead, a mixture of distribution is used to modulate the data and classify them into several subgroups. The mixture of Rayleigh distribution is best to be used with the lifetime process. This paper aims to infer model parameters by the expectation-maximization (EM) algorithm through the maximum likelihood function. The technique is applied to simulated data by following several scenarios. The accuracy of estimation has been examined by the average mean square error (AMSE) and the average classification success rate (ACSR). T

Exploring the B-Spline Transform for Estimating Lévy Process Parameters: Applications in Finance and Biomodeling Exploring the B-Spline Transform for Estimating Lévy Process Parameters: Applications in Finance and Biomodeling Letters in Biomathematics · Jul 7, 2025Letters in Biomathematics · Jul 7, 2025 Show publication This paper, presents the application of the B-spline transform as an effective and precise technique for estimating key parameters i.e., drift, volatility, and jump intensity for Lévy processes. Lévy processes are powerful tools for representing phenomena with continuous trends with abrupt changes. The proposed approach is validated through a simulated biological case study on animal migration in which movements are mo

The goal of the study is to discover the best model for forecasting the exchange rate of the US dollar against the Iraqi dinar by analyzing time series using the Box Jenkis approach, which is one of the most significant subjects in the statistical sciences employed in the analysis. The exchange rate of the dollar is considered one of the most important determinants of the relative level of the health of the country's economy. It is considered the most watched, analyzed and manipulated measure by the government. There are factors affecting in determining the exchange rate, the most important of which are the amount of money, interest rate and local inflation global balance of payments. The data for the research that represents the exchange r

المستخلص ان عملية تقدير الانموذج وأختيار المتغير المعنوي هي عملية حاسمة في النمذجة شبه المعلميه semi-parametric modeling)) ففي بدايه عملية النمذجة كثيرا" ما يكون هنالك عدد كبير من المتغيرات التوضيحية لتجنب فقدان أي عناصر تفسيريه قد تكون هامة ونتيجة لذلك فأن أختيار المتغيرات المعنوية أصبحت ضرورة فضلاً عن ان عملية أختيار المتغير ليس الغرض منه تبسيط الأنموذج المعقد وتفسيره فقط ولكن كذلك القدرة على التنبؤ . في هذا ا

في هذا البحث تمت دراسة احد نماذج العمليات  العشوائيه التصادفية وهو احد نماذج   le'vyمعتمدين على مايسمى بالحركه البراونيه ذي الأحداثيات الجزئية Brownia subordinate. اذ تم الاعتماد على ما يسمى بأنموذج معكوس كاوس الطبيعي  Normal Inverse Gassian (NIG اذ يهدف هذا البحث الى تقدير معالم ذلك الأنموذج بأستعمال طريقتي العزوم والأمكان الأعظم , ومن ثم توظيف تلك المقدرات للمعالم في دراسة عوائد الأسهم وتقييم اصول التسعير للمصرف المتحد ومصر

يهدف هذا البحث الى تقدير الانموذج الخطي الجزئي (Partial Linear Regression Model) بإستعمال طريقتين من طرائق التمهيد وهما طريقتي التمهيد المويجي (Wavelet Smoother)  والتمهيد اللبي (Kernel Smoother). تم استعمال تجارب المحاكات لبيان افضل تلك الطرائق في تقدير هذا النوع من النماذج بإختلاف الحالات والدوال وحجوم العينات والتباينات المستعملة. تم الاعتماد على معيار معدل متوسط مربعات الخطأ (Mean Average Squares Error) كأحد معايير المقارنة بين الطرائق. واثب

ان الهدف من الدراسة هو بيان القدرة التنبؤية الافضل بين انموذج الانحدار اللوجستي والدالة المميزة الخطية باستعمال البيانات الاصليه اولا ثم المركبات الرئيسة لتقليص الابعاد بين المتغيرات لبيانات المسح الاجتماعي والاقتصادي للاسرة لمحافظة بغداد لعام 2012 وتضمنت عينة البحث 615 مفردة لـ13 متغير، 12منها متغير توضيحي والمتغير المعتمد شمل العاملين والعاطلين عن العمل، تم اجراء المقارنة بين الطريقتين اعلاه واتضح من خلال

This Book is intended to be textbook studied for undergraduate course in multivariate analysis. This book is designed to be used in semester system. In order to achieve the goals of the book, it is divided into the following chapters. Chapter One introduces matrix algebra. Chapter Two devotes to Linear Equation System Solution with quadratic forms, Characteristic roots & vectors. Chapter Three discusses Partitioned Matrices and how to get Inverse, Jacobi and Hessian matrices. Chapter Four deals with Multivariate Normal Distribution (MVN). Chapter Five concern with Joint, Marginal and Conditional Normal Distribution, independency and correlations. Many solved examples are intended in this book, in addition to a variety of unsolved relied pro

Nonparametric methods are used in the data that contain outliers values. The main importance in using Nonparametric methods is to locate the median in the multivariate regression model. It is difficult to locate the median due to the presence of more than one dimension and the dispersion of values and the increase of the studied phenomenon data.The genetic algorithms Minimum Weighted Covariance Determinant Estimator (MWCD), was applied and compared with the multilayer neural network Back propagation to find the estimate of the median location based on the minimum distance (Mahalanobis Distance) and smallest specified for the variance matrix. Joint Minimum Covariance Determinant (MCD) as one of the most nonparametric methods robust. The stud

This study is dedicated to solving multicollinearity problem for the general linear model by using Ridge regression method. The basic formulation of this method and suggested forms for Ridge parameter is applied to the Gross Domestic Product data in Iraq. This data has normal distribution. The best linear regression model is obtained after solving multicollinearity problem with the suggesting of 10 k value.

<span lang="EN-US">Usability evaluation is a core usability activity that minimizes risks and improves product quality. The returns from usability evaluation are undeniable. Neglecting such evaluation at the development stage negatively affects software usability. In this paper, the authors develop a software management tool used to incorporate usability evaluation activities into the agile environment. Using this tool, agile development teams can manage a continuous evaluation process, tightly coupled with the development process, allowing them to develop high quality software products with adequate level of usability. The tool was evaluated through verification, followed by the validation on satisfaction. The evaluation resu

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

This paper study two stratified quantile regression models of the marginal and the conditional varieties. We estimate the quantile functions of these models by using two nonparametric methods of smoothing spline (B-spline) and kernel regression (Nadaraya-Watson). The estimates can be obtained by solve nonparametric quantile regression problem which means minimizing the quantile regression objective functions and using the approach of varying coefficient models. The main goal is discussing the comparison between the estimators of the two nonparametric methods and adopting the best one between them

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.

Statistical methods of forecasting have applied with the intention of constructing a model to predict the number of the old aged people in retirement homes in Iraq. They were based on the monthly data of old aged people in Baghdad and the governorates except for the Kurdistan region from 2016 to 2019. Using Box-Jenkins methodology, the stationarity of the series was examined. The appropriate model order was determined, the parameters were estimated, the significance was tested, adequacy of the model was checked, and then the best model of prediction was used. The best model for forecasting according to criteria of (Normalized BIC, MAPE, RMSE) is ARIMA (0, 1, 2).