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Comparison for estimation methods for the autoregressive approximations
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Abstract

      In this study, we compare between the autoregressive approximations (Yule-Walker equations, Least Squares , Least Squares ( forward- backword ) and Burg’s (Geometric and Harmonic ) methods, to determine the optimal approximation to the time series generated from the first - order moving Average non-invertible process, and fractionally - integrated noise process, with several values for d (d=0.15,0.25,0.35,0.45) for different sample sizes (small,median,large)for two processes . We depend on figure of merit function which proposed by author Shibata in 1980, to determine the theoretical optimal order according to minimum value for Figure of merit functio, for several values for h (h=1 to) to compare between estimation methods  which used in this study. Also we based On Information Criteria which imply (Akaike, Final Prediction Error, Mallow , s statistic, Parzen , Bhansali). Addition to several Criteria based on values for estimation parameters such as Mean Error Estimation ( MEE ), Mean  Square Error Estimation ( MSEE ) and  Mean  Absolute Error Estimation ( MAEE ) .The results are obtained by using simulation  which is based on Matlab programms.

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Publication Date
Sun Jun 01 2014
Journal Name
University Of Baghdad
New Analytical Methods for Drugs Analysis A Comparative Study
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Four different spectrophotometric methods are used in this study for the determination of Sulfamethoxazole and sulfanilamide drugs in pharmaceutical compounds, synthetic samples, and in their pure forms. The work comprises four chapters which are shown in the following: Chapter One: Includes a brief for Ultraviolet-Visible (UV-VIS) Absorption spectroscopy, antibacterial drugs and sulfonamides with some methods for their determination. The chapter lists two methods for optimization; univariate method and multivariate method. The later includes different types, two of these were mentioned; simplex method and design of experiment method. Chapter Two: Includes reaction of the two studied drugs with sodium nitrite and hydrochloric acid for diazo

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Publication Date
Mon Jan 20 2020
Journal Name
Kuwait Journal Of Science
Three iterative methods for solving Jeffery-Hamel flow problem
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In this article, the nonlinear problem of Jeffery-Hamel flow has been solved analytically and numerically by using reliable iterative and numerical methods. The approximate solutions obtained by using the Daftardar-Jafari method namely (DJM), Temimi-Ansari method namely (TAM) and Banach contraction method namely (BCM). The obtained solutions are discussed numerically, in comparison with other numerical solutions obtained from the fourth order Runge-Kutta (RK4), Euler and previous analytic methods available in literature. In addition, the convergence of the proposed methods is given based on the Banach fixed point theorem. The results reveal that the presented methods are reliable, effective and applicable to solve other nonlinear problems.

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Publication Date
Sat Mar 20 2021
Journal Name
Agroforestry Systems
Hydraulic lift: processes, methods, and practical implications for society
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Publication Date
Thu Jun 29 2023
Journal Name
Wasit Journal For Pure Sciences
Suitable Methods for Solving COVID-19 Model in Iraq
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Because the Coronavirus epidemic spread in Iraq, the COVID-19 epidemic of people quarantined due to infection is our application in this work. The numerical simulation methods used in this research are more suitable than other analytical and numerical methods because they solve random systems. Since the Covid-19 epidemic system has random variables coefficients, these methods are used. Suitable numerical simulation methods have been applied to solve the COVID-19 epidemic model in Iraq. The analytical results of the Variation iteration method (VIM) are executed to compare the results. One numerical method which is the Finite difference method (FD) has been used to solve the Coronavirus model and for comparison purposes. The numerical simulat

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Publication Date
Thu Sep 01 2022
Journal Name
Journal Of Engineering
Methods for Removing Dyes from Polluted Water; A Review
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Most of the water pollutants with dyes are leftovers from industries, including textiles, wool and others. There are many ways to remove dyes such as sorption, oxidation, coagulation, filtration, and biodegradation, Chlorination, ozonation, chemical precipitation, adsorption, electrochemical processes, membrane approaches, and biological treatment are among the most widely used technologies for removing colors from wastewater. Dyes are divided into two types: natural dyes and synthetic dyes.

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Publication Date
Wed Oct 01 2014
Journal Name
Journal Of Economics And Administrative Sciences
Spatial Regression Model Estimation for the poverty Rates In the districts of Iraq in 2012
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Theresearch took the spatial autoregressive model: SAR and spatial error model: SEM in an attempt to provide a practical evident that proves the importance of spatial analysis, with a particular focus on the importance of using regression models spatial andthat includes all of them spatial dependence, which we can test its presence or not by using Moran test. While ignoring this dependency may lead to the loss of important information about the phenomenon under research is reflected in the end on the strength of the statistical estimation power, as these models are the link between the usual regression models with time-series models. Spatial analysis had

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Publication Date
Tue Jun 03 2025
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Comparison of some artificial neural networks for graduate students
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Artificial Neural Networks (ANN) is one of the important statistical methods that are widely used in a range of applications in various fields, which simulates the work of the human brain in terms of receiving a signal, processing data in a human cell and sending to the next cell. It is a system consisting of a number of modules (layers) linked together (input, hidden, output). A comparison was made between three types of neural networks (Feed Forward Neural Network (FFNN), Back propagation network (BPL), Recurrent Neural Network (RNN). he study found that the lowest false prediction rate was for the recurrentt network architecture and using the Data on graduate students at the College of Administration and Economics, Univer

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Publication Date
Tue Oct 23 2018
Journal Name
Journal Of Economics And Administrative Sciences
Processing of missing values in survey data using Principal Component Analysis and probabilistic Principal Component Analysis methods
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The idea of ​​carrying out research on incomplete data came from the circumstances of our dear country and the horrors of war, which resulted in the missing of many important data and in all aspects of economic, natural, health, scientific life, etc.,. The reasons for the missing are different, including what is outside the will of the concerned or be the will of the concerned, which is planned for that because of the cost or risk or because of the lack of possibilities for inspection. The missing data in this study were processed using Principal Component  Analysis and self-organizing map methods using simulation. The variables of child health and variables affecting children's health were taken into account: breastfeed

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Publication Date
Fri Dec 01 2017
Journal Name
Journal Of Economics And Administrative Sciences
مقارنة بين طريقتي مقدرات لابلاس وهوبر الحصين لتقدير معلمات أنموذج الانحدار اللوجستي
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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

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Publication Date
Mon Dec 05 2022
Journal Name
Baghdad Science Journal
K-Nearest Neighbor Method with Principal Component Analysis for Functional Nonparametric Regression
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This paper proposed a new  method to study functional non-parametric regression data analysis with conditional expectation in the case that the covariates  are functional and the Principal Component Analysis was utilized to de-correlate the multivariate response variables. It  utilized the formula of the Nadaraya Watson estimator (K-Nearest Neighbour (KNN)) for prediction with different types of the semi-metrics, (which are based on Second Derivative and Functional Principal Component Analysis (FPCA))  for measureing the closeness between curves.  Root Mean Square Errors is used for the  implementation of this model which is then compared to the independent response method. R program is used for analysing data. Then, when  the cov

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