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Application the generalized estimating equation Method (GEE) to estimate of conditional logistic regression model for repeated measurements
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Conditional logistic regression is often used to study the relationship between event outcomes and specific prognostic factors in order to application of logistic regression and utilizing its predictive capabilities into environmental studies. This research seeks to demonstrate a novel approach of implementing conditional logistic regression in environmental research through inference methods predicated on longitudinal data. Thus, statistical analysis of longitudinal data requires methods that can properly take into account the interdependence within-subjects for the response measurements. If this correlation ignored then inferences such as statistical tests and confidence intervals can be invalid largely.

      For estimating the conditional regression model in the analysis of environment pollution as a function of oil production and environmental factors using the generalized estimating equation (GEE) in the formulation of inference methods that facilitate the conditional logistic regression model taking advantage of the actual correlations between responses in the data, as well as the specific correlation structure through robust sandwich estimators (RSE) as well as application many of various model selection criteria. Because the efficiency of estimates is contingent on the working correlation matrix specification, the appropriate selection of a working correlation matrix can significantly advance the GEE statistical inference efficiency. After comparing the performance of specific criteria indicating that QIC is the selection criterion that is most suited for GEE method. The application results showed that QIC had the lowest information loss in GEE method in which the objective to develop a predictive model of the candidate set, Through this research, condition logistic regression has also been demonstrated to be an effective tool that can be used in other studies to explore the relationships between response and explanatory variables.

 

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Publication Date
Sat Dec 28 2019
Journal Name
Journal Of Mechanics Of Continua And Mathematical Sciences
NEW ROBUST ESTIMATOR OF CHANGE POINT IN SEGMENTED REGRESSION MODEL FOR BED-LOAD OF RIVERS
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Publication Date
Sat Jan 01 2022
Journal Name
Ssrn Electronic Journal
The Prospective of Artificial Neural Network (ANN’s) Model Application to Ameliorate Management of Post Disaster Engineering Projects
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Currently and under the COVID-19 which is considered as a kind of disaster or even any other natural or manmade disasters, this study was confirmed to be important especially when the society is proceeding to recover and reduce the risks of as possible as injuries. These disasters are leading somehow to paralyze the activities of society as what happened in the period of COVID-19, therefore, more efforts were to be focused for the management of disasters in different ways to reduce their risks such as working from distance or planning solutions digitally and send them to the source of control and hence how most countries overcame this stage of disaster (COVID-19) and collapse. Artificial intelligence should be used when there is no practica

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Publication Date
Sun Dec 01 2013
Journal Name
Journal Of Economics And Administrative Sciences
CALCULATION BIASES FOR COEFFICIENTS AND SCALE PARAMETER FOR LINEAR (TYPE 1) EXTREME VALUE REGRESSION MODEL FOR LARGEST VALUES
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Abstract

Characterized by the Ordinary Least Squares (OLS) on Maximum Likelihood for the greatest possible way that the exact moments are known , which means that it can be found, while the other method they are unknown, but approximations to their biases correct to 0(n-1) can be obtained by standard methods. In our research expressions for approximations to the biases of the ML estimators (the regression coefficients and scale parameter) for linear (type 1) Extreme Value Regression Model for Largest Values are presented by using the advanced approach depends on finding the first derivative, second and third.

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Publication Date
Fri Feb 01 2019
Journal Name
Journal Of Economics And Administrative Sciences
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

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Publication Date
Sun Jun 01 2008
Journal Name
Baghdad Science Journal
The Datums Transformation for GPS Navigation Measurements Correction
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The GPS navigation measurements become more widely used in many civilian and scientific application. All GPS navigation data holds many errors, the main error sources arise from the geodetic Datum variation when user apply the GPS measurements with the map. Geodetic datums define the size and shape of the earth and the origin and orientation of the coordinate systems used to map the earth surface. In this paper, the Datum transformation was evaluated in two mathematical methods to overcome the errors due to the difference between the WGS-84 and our country Datum Clarck-1880. The results was evaluated and investigated using Carmin GPS device for GCPs comparison, topographic map for Hilla city, mid Iraq 1:100000 scale, and two georefrencing

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Publication Date
Thu Dec 01 2016
Journal Name
Journal Of Economics And Administrative Sciences
Usage of non-linear programming in building a mathematical model for production planning according to discount constraints put on bought amount
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Abstract

 This research deals will the declared production planning operation in the general company of planting oils, which have  great role in production operations management who had built mathematical model for correct non-linear programming according to discounting operation during raw materials or half-made materials purchasing operation which concentration of six main products by company but discount included just three products of raw materials, and there were six months taken from the 1st half of 2014 as a planning period has been chosen . Simulated annealing algorithm  application on non-linear model which been more difficulty than possible solution when imposed restric

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Publication Date
Wed Nov 30 2022
Journal Name
Iraqi Geological Journal
A Predictive Model for Estimating Unconfined Compressive Strength from Petrophysical Properties in the Buzurgan Oilfield, Khasib Formation, Using Log Data
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Unconfined compressive strength (UCS) of rock is the most critical geomechanical property widely used as input parameters for designing fractures, analyzing wellbore stability, drilling programming and carrying out various petroleum engineering projects. The USC regulates rock deformation by measuring its strength and load-bearing capacity. The determination of UCS in the laboratory is a time-consuming and costly process. The current study aims to develop empirical equations to predict UCS using regression analysis by JMP software for the Khasib Formation in the Buzurgan oil fields, in southeastern Iraq using well-log data. The proposed equation accuracy was tested using the coefficient of determination (R²), the average absolute

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Publication Date
Fri Jan 01 2016
Journal Name
Lap Lambert Academic Publishing
New Technique to Estimate the concentration of Heavy Metals in soil
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There are many aims of this book: The first aim is to develop a model equation that describes the spread of contamination through soils which can be used to determine the rate of environmental contamination by estimate the concentration of heavy metals (HMs) in soil. The developed model equation can be considered as a good representation for a problem of environmental contamination. The second aim of this work is to design two feed forward neural networks (FFNN) as an alternative accurate technique to determine the rate of environmental contamination which can be used to solve the model equation. The first network is to simulate the soil parameters which can be used as input data in the second suggested network, while the second network sim

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Publication Date
Sat Sep 30 2023
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
The Extent of Applying Corporate Governance Requirements According to International Specification (ISO 26000:2010) in the National Insurance Company: A Research Application
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Abstract

                The research aims to identify the application of corporate governance requirements according to the international standard (ISO 26000:2010) in the National Insurance Company. Strengths and weaknesses were identified to study the current state of this requirement's application in the company under investigation. The descriptive-analytical approach was utilized through a checklist derived from ISO 26000:2010. Several personal interviews and field visits were conducted to understand the extent of application and documentation based on various statistical methods. The results revealed a level of applic

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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
The Effect Of Optimizers On The Generalizability Additive Neural Attention For Customer Support Twitter Dataset In Chatbot Application
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When optimizing the performance of neural network-based chatbots, determining the optimizer is one of the most important aspects. Optimizers primarily control the adjustment of model parameters such as weight and bias to minimize a loss function during training. Adaptive optimizers such as ADAM have become a standard choice and are widely used for their invariant parameter updates' magnitudes concerning gradient scale variations, but often pose generalization problems. Alternatively, Stochastic Gradient Descent (SGD) with Momentum and the extension of ADAM, the ADAMW, offers several advantages. This study aims to compare and examine the effects of these optimizers on the chatbot CST dataset. The effectiveness of each optimizer is evaluat

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