An Efficient Shrinkage Estimator for the Parameters of Simple Linear Regression Model
Publication Date
Wed Oct 17 2018
Journal Name
Journal Of Economics And Administrative Sciences
discriminate analysis and logistic regression by use partial least square
الدالة المميزة الخطية – الانحدار اللوجستي الثنائي – المربعات الصغرى الجزئية – مشكلة التعدد الخطي – نسبة التصنيف – محاكاة.
linear discriminant function- binary logistic regression- partial least square– multicollinearity problem – ratio of classification – simulation.
Abstract
The method binery logistic regression and linear discrimint function of the most important statistical methods used in the classification and prediction when the data of the kind of binery (0,1) you can not use the normal regression therefore resort to binary logistic regression and linear discriminant function in the case of two group in the case of a Multicollinearity problem between the data (the data containing high correlation) It became not possible to use binary logistic regression and linear discriminant function, to solve this problem, we resort to Partial least square regression.
In this, search th
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Publication Date
Fri Jul 23 2021
Journal Name
International Journal Of Dentistry
Predicting Canine and Premolar Mesiodistal Crown Diameters Using Regression Equations
Objectives. The current study aimed to predict the combined mesiodistal crown widths of maxillary and mandibular canines and premolars from the combined mesiodistal crown widths of maxillary and mandibular incisors and first molars. Materials and Methods. This retrospective study utilized 120 dental models from Iraqi Arab young adult subjects with normal dental relationships. The mesiodistal crown widths of all teeth (except the second molars) were measured at the level of contact points using digital electronic calipers. The relation between the sum mesiodistal crown widths of the maxillary and mandibular incisors and first molars and the combined mesiodistal crown widths of the maxillary and mandibular canines and premolars was as
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(3)
Publication Date
Wed Oct 05 2016
Journal Name
Anbar Journal Of Agricultural Sciences
Estimation of genetic parameters in cowpea
genetic parameters
cowpea
hybridization
parents
reciprocal hybrids
Metrics
An experiment was carried out in the fields of Agriculture College-Baghdad University during spring and autumn of 2015 by using a randomized complete blocks design with three replications. The first season hybridization was established among three pure cultivars of cowpea (Vigna uniguiculata L.) which: Ramshorn, California black eye and Rahawya in full diallel crosses according to Griffing with first method and fixed model (3 parents+ 3 diallel hybrids +3 reciprocal hybrids) and a comparison experiment was in autumn season. The result of statistical analysis showed that there was a significant difference among the parents and their hybrids for all the studied characters. The parent 1 was the higher for root nodules number , leaf number, pod
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Tue Oct 23 2018
Journal Name
Journal Of Economics And Administrative Sciences
Use projection pursuit regression and neural network to overcome curse of dimensionality
:- curse of dimensionality
projection pursuit regression
neural networks.
Abstract
This research aim to overcome the problem of dimensionality by using the methods of non-linear regression, which reduces the root of the average square error (RMSE), and is called the method of projection pursuit regression (PPR), which is one of the methods for reducing dimensions that work to overcome the problem of dimensionality (curse of dimensionality), The (PPR) method is a statistical technique that deals with finding the most important projections in multi-dimensional data , and With each finding projection , the data is reduced by linear compounds overall the projection. The process repeated to produce good projections until the best projections are obtained. The main idea of the PPR is to model
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(1)
Publication Date
Mon Nov 11 2019
Journal Name
Spe
Modeling Rate of Penetration using Artificial Intelligent System and Multiple Regression Analysis
Abstract<p>Over the years, the prediction of penetration rate (ROP) has played a key rule for drilling engineers due it is effect on the optimization of various parameters that related to substantial cost saving. Many researchers have continually worked to optimize penetration rate. A major issue with most published studies is that there is no simple model currently available to guarantee the ROP prediction.</p><p>The main objective of this study is to further improve ROP prediction using two predictive methods, multiple regression analysis (MRA) and artificial neural networks (ANNs). A field case in SE Iraq was conducted to predict the ROP from a large number of parame</p>
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neural network
strength
machine learning
ROP prediction
Upstream Oil & Gas
prediction
ANN model
input parameter
multiple regression analysis
artificial neural network
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Publication Date
Sat Jun 01 2024
Journal Name
Results In Engineering
(28)
(22)
Publication Date
Fri Feb 01 2019
Journal Name
Journal Of Physics: Conference Series
Publication Date
Fri Feb 01 2019
Journal Name
Journal Of Physics: Conference Series
Publication Date
Fri Apr 01 2016
Journal Name
Journal Of Economics And Administrative Sciences
Comparing Bayes Estimators With others , for scale parameter and Reliability function of two parameters Frechet distribution
مقدر الإمكان الأعظم (MLE)
مقدر Bayes الأول
مقدر Bayes الثاني.
Maximum likelihood estimator (MLE) Bayes (1) and Bayes (2) Mean square error (E) is shape parameter is scale parameter under Quadratic loss function under General Entropy loss function
Publication Date
Thu Feb 01 2018
Journal Name
Italian Journal Of Pure And Applied Mathematics
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