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jeasiq-969
Comparing Several Nonlinear Estimators for Regression Function
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The aim of this paper is to estimate a nonlinear regression function of the Export of the crude oil Saudi (in Million Barrels) as a function of the number of discovered fields.

 Through studying the behavior of the data we show that its behavior was not followed a linear pattern or can put it in a known form so far there was no possibility to see a general trend resulting from such exports.

We use different nonlinear estimators to estimate a regression function, Local linear estimator, Semi-parametric as well as an artificial neural network estimator (ANN).

The results proved that the (ANN) estimator is the best nonlinear estimator among the others in estimating the export of crude oil Saudi.

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Publication Date
Mon Apr 03 2023
Journal Name
Journal Of Electronics,computer Networking And Applied Mathematics
Comparison of Some Estimator Methods of Regression Mixed Model for the Multilinearity Problem and High – Dimensional Data
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In order to obtain a mixed model with high significance and accurate alertness, it is necessary to search for the method that performs the task of selecting the most important variables to be included in the model, especially when the data under study suffers from the problem of multicollinearity as well as the problem of high dimensions. The research aims to compare some methods of choosing the explanatory variables and the estimation of the parameters of the regression model, which are Bayesian Ridge Regression (unbiased) and the adaptive Lasso regression model, using simulation. MSE was used to compare the methods.

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Publication Date
Sun Jun 01 2014
Journal Name
Baghdad Science Journal
Synthesis, Characterization and study biologicalactivity of several 1-cyclopentene-1,2-dicarboxylimidyl Containing oxadiazole and Benzothiazole
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In this work involved prepared of several new 1-cyclopentene-1,2-dicarboxylimide linked to oxadiazole and benzothiazole moiety were synthesized by two steps: The first step 2-amino-substituted-1,3,4-oxadiazoles and substituted-2-aminobenzothiazole were reaction with 1-cyclopentene-1,2-dicarboxyl anhydride producing N-( 5- substituted-1,3,4-oxadiazole-2-yl)-1-cyclopentene-1,2-dicarboxyl amic acids and N-(Substitutedbenzothiazole-2-yl)-1-cyclopentene-1,2-dicarboxyl amic acids which in turn were dehydrated in the second step via fusion method to afford he desirable N-(5-substituted-1,3,4-oxadiazole-2-yl)-1-cyclopentene-1,2-dicarboxylimides and N-(Substituted benzothiazole-2-yl)1-cyclopentene-1,2-dicarboxylimides respectively. Struct

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Publication Date
Tue Dec 22 2015
Journal Name
International Journal Of Current Microbiology And Applied Sciences
Revision to the genera of Leaf Miner Agromyzydae (Insecta: Diptera) in Several Regions of Iraq
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Publication Date
Sun Jan 01 2023
Journal Name
8th Engineering And 2nd International Conference For College Of Engineering – University Of Baghdad: Coec8-2021 Proceedings
Cascade position-torque control strategy based on function approximation technique (FAT) for flexible joint robots
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Publication Date
Tue Apr 26 2011
Journal Name
Evolutionary Algorithms
Variants of Hybrid Genetic Algorithms for Optimizing Likelihood ARMA Model Function and Many of Problems
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Publication Date
Thu Dec 01 2011
Journal Name
Journal Of Economics And Administrative Sciences
Determine the optimal policy for the function of Pareto distribution reliability estimated using dynamic programming
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The goal (purpose) from using development technology that require mathematical procedure related with high Quality & sufficiency of solving complex problem called Dynamic Programming with in recursive method (forward & backward) through  finding series of associated decisions for reliability function of Pareto distribution estimator by using two approach Maximum likelihood & moment .to conclude optimal policy

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Publication Date
Mon Jul 05 2010
Journal Name
Evolutionary Algorithms
Variants of Hybrid Genetic Algorithms for Optimizing Likelihood ARMA Model Function and Many of Problems
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Optimization is essentially the art, science and mathematics of choosing the best among a given set of finite or infinite alternatives. Though currently optimization is an interdisciplinary subject cutting through the boundaries of mathematics, economics, engineering, natural sciences, and many other fields of human Endeavour it had its root in antiquity. In modern day language the problem mathematically is as follows - Among all closed curves of a given length find the one that closes maximum area. This is called the Isoperimetric problem. This problem is now mentioned in a regular fashion in any course in the Calculus of Variations. However, most problems of antiquity came from geometry and since there were no general methods to solve suc

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Publication Date
Sun Mar 25 2018
Journal Name
Biomedical And Pharmacology Journal
Comparing the Impact Strength and Transverse Flexure Strength of Three Different Dentures Base Materials
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Publication Date
Sun Oct 01 2023
Journal Name
Indonesian Journal Of Electrical Engineering And Computer Science
Intelligence framework dust forecasting using regression algorithms models
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<span>Dust is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. In the recent decade, climate change in Iraq, typified by increased droughts and deserts, has generated numerous environmental issues. This study forecasts dust in five central Iraqi districts using machine learning and five regression algorithm supervised learning system framework. It was assessed using an Iraqi meteorological organization and seismology (IMOS) dataset. Simulation results show that the gradient boosting regressor (GBR) has a mean square error of 8.345 and a total accuracy ratio of 91.65%. Moreover, the results show that the decision tree (DT), where the mean square error is 8.965, c

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Publication Date
Mon Feb 01 2021
Journal Name
Journal Of Physics: Conference Series
Bayesian Computational Methods of the Logistic Regression Model
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Abstract<p>In this paper, we will discuss the performance of Bayesian computational approaches for estimating the parameters of a Logistic Regression model. Markov Chain Monte Carlo (MCMC) algorithms was the base estimation procedure. We present two algorithms: Random Walk Metropolis (RWM) and Hamiltonian Monte Carlo (HMC). We also applied these approaches to a real data set.</p>
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