An Efficient Shrinkage Estimator for the Parameters of Simple Linear Regression Model
Publication Date
Sun Dec 01 2019
Journal Name
2019 First International Conference Of Computer And Applied Sciences (cas)
(10)
(3)
Publication Date
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
(8)
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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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
(2)
Publication Date
Sun Mar 02 2008
Journal Name
Baghdad Science Journal
Orthogonal Functions Solving Linear functional Differential EquationsUsing Chebyshev Polynomial
A method for Approximated evaluation of linear functional differential equations is described. where a function approximation as a linear combination of a set of orthogonal basis functions which are chebyshev functions .The coefficients of the approximation are determined by (least square and Galerkin’s) methods. The property of chebyshev polynomials leads to good results , which are demonstrated with examples.
(4)