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jeasiq-1599
A Comparison of Parameters Estimation Methods for the Negative Binomial Regression Model under Multicollinearity Problem by Using Simulation
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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.

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Publication Date
Wed Mar 10 2021
Journal Name
Baghdad Science Journal
A Comparison Between the Theoretical Cross Section Based on the Partial Level Density Formulae Calculated by the Exciton Model with the Experimental Data for (_79^197)Au nucleus
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In this paper, the theoretical cross section in pre-equilibrium nuclear reaction has been studied for the reaction  at energy 22.4 MeV. Ericson’s formula of partial level density PLD and their corrections (William’s correction and spin correction) have been substituted  in the theoretical cross section and compared with the experimental data for  nucleus. It has been found that the theoretical cross section with one-component PLD from Ericson’s formula when  doesn’t agree with the experimental value and when . There is little agreement only at the high value of energy range with  the experimental cross section. The theoretical cross section that depends on the one-component William's formula and on-component corrected to spi

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Scopus (7)
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Publication Date
Tue Aug 18 2020
Journal Name
Journal Of Mechanics Of Continua And Mathematical Sciences
THE COMPARISON OF THE METHODS ESTIMATING THE FRACTIONAL DIFFERENCES OF PARAMETER AND ITS DEPENDENCE ON ESTIMATION THE BEST LINEAR MODEL OF TIME SERIES IN THE ENVIRONMENTAL FIELD
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Publication Date
Sun Dec 31 2006
Journal Name
Journal Of Engineering
Theoretical Simulation Of Stress-Strain Relations For Some Iraqiclays Using The Endochronic Model
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Publication Date
Wed Sep 01 2010
Journal Name
Journal Of Economics And Administrative Sciences
Using simulation to estimate parameters and reliability function for extreme value distribution
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   This study includes Estimating scale parameter, location parameter  and reliability function  for Extreme Value (EXV) distribution by two methods, namely: -
- Maximum Likelihood Method (MLE).
- Probability Weighted Moments Method (PWM).

 Used simulations to generate the required samples to estimate the parameters and reliability function of different sizes(n=10,25,50,100) , and give real values for the parameters are and , replicate the simulation experiments (RP=1000)

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Publication Date
Tue Dec 12 2017
Journal Name
Al-khwarizmi Engineering Journal
Design a Vegetative Filter Strips Length Using VFSMOD_W Model for Reducing Sediments and Pesticides
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 Abstract 

The vegetative filter strips (VFS) are a useful tool used for reducing the movement of sediment and pesticide in therivers. The filter strip’s soil can help in reducing the runoff volume by infiltration. However, the characteristics of VFS (i.e., length) are not recently identified depending on the estimation of VFS modeling performance. The aim of this research is to study these characteristics and determine acorrelation between filter strip length and percent reduction (trapping efficiency) for sediment, water, and pesticide. Two proposed pesticides(one has organic carbon sorption coefficient, Koc, of 147 L/kg which is more moveable than XXXX, and another one

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Publication Date
Sun Jun 01 2014
Journal Name
Journal Of Economics And Administrative Sciences
Different Methods for Estimating Location Parameter & Scale Parameter for Extreme Value Distribution
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      In this study, different methods were used for estimating location parameter  and scale parameter for extreme value distribution, such as maximum likelihood estimation (MLE) , method of moment  estimation (ME),and approximation  estimators based on percentiles which is called white method in estimation, as the extreme value distribution is one of exponential distributions. Least squares estimation (OLS) was used, weighted least squares estimation (WLS), ridge regression estimation (Rig), and adjusted ridge regression estimation (ARig) were used. Two parameters for expected value to the percentile  as estimation for distribution f

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Publication Date
Mon Jun 01 2015
Journal Name
Journal Of Economics And Administrative Sciences
Using the Logistic Regression Model in Studding the Assistant Factors to Diagnose Bladder Cancer
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The cancer is one of the biggest health problems that facing the world . And  the bladder cancer has a special place among the most spread cancers in Arab countries specially in Iraq and Egypt(2) . It is one of the diseases which can be treated and cured if it is diagnosed early . This research is aimed at studying the assistant factors that diagnose bladder cancer such as (patient's age , gender , and other major complains of hematuria , burning or pain during urination and micturition disorders) and then determine which factors are the most effective in the possibility of diagnosing this disease by using the statistical model (logistic regression model) and depending on a random sample of (128) patients . After

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Publication Date
Tue Jun 01 2010
Journal Name
Journal Of Economics And Administrative Sciences
مقارنة بعض طرائق التعويض الأحادي للبيانات المفقودة لدالة الكثافة الاحتمالية للتوزيع الطبيعي ثنائي المتغيرات
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In this paper we suggest new method to estimate the missing data  in bivariate normal distribution and compare it with Single Imputation  method (Unconditional mean and Conditional mean) by using simulation. 

 

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Publication Date
Mon Nov 30 2020
Journal Name
Journal Of Economics And Administrative Sciences
Building a model to reduce the cost of hajj in the Iraqi Authority for Hajj and Umrah
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The research is summarized in the construction of a mathematical model using the most common methods in the science of Operations Research, which are the models of transportation and linear programming to find the best solution to the problem of the high cost of hajj in Iraq, and this is done by reaching the optimum number of pilgrims traveling through both land ports and the number Ideal for passengers traveling through airports by Iraqi Airways, instead of relying on the personal experience of the decision-maker in Hajj and Umrah Authority by identifying the best port for pilgrim's travel, which can tolerate right or wrong, has been based on scientific methods of Operations Research, the researcher built two mathematical models

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Crossref
Publication Date
Wed Oct 17 2018
Journal Name
Journal Of Economics And Administrative Sciences
The Use Of the Bayesian Method and Restricted Maximum Likelihood in estimating of mixed Linear Components with random effects model with practical application.
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In this research we study a variance component model, Which is the one of the most important models widely used in the analysis of the data, this model is one type of a multilevel models, and it is considered as linear models , there are three types of linear variance component models ,Fixed effect of linear variance component model, Random effect of linear variance component model and Mixed effect of linear variance component model . In this paper we will examine the model of mixed effect of linear variance component model with one –way random effect ,and the mixed model is a mixture of fixed effect and random effect in the same model, where it contains the parameter (μ) and treatment effect (τi ) which  has

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