Results 21 to 30 of about 25,104 (288)
On the Performance of Jackknife Based Estimators for Ridge Regression
IEEE Access, 2021 تُستخدم تقنيات الانحدار بشكل عام للتنبؤ بمتغير الاستجابة باستخدام متغير تنبؤ واحد أو أكثر. في العديد من مجالات الدراسة، يمكن أن تكون الانحدارات مترابطة للغاية، مما يؤدي إلى مشكلة الخطية المتعددة. وبالتالي، تصبح تقديرات المربعات الصغرى العادية غير متسقة وتؤدي إلى استنتاجات خاطئة. للتعامل مع المشكلة، تُستخدم تقنيات التعلم الآلي على وجه الخصوص، نهج انحدار
Ismail Shah +5 more
openaire +2 more sources
Two-Parameter Modified Ridge-Type M-Estimator for Linear Regression Model
The Scientific World Journal, 2020 The general linear regression model has been one of the most frequently used models over the years, with the ordinary least squares estimator (OLS) used to estimate its parameter.
Adewale F. Lukman +3 more
doaj +1 more source
Kuwait Journal of Science, 2023 Ridge regression is employed to estimate the regression parameters while circumventing the multicollinearity among independent variables. The ridge parameter plays a vital role as it controls bias-variance tradeoff. Several methods for choosing the ridge
Irum Sajjad Dar +3 more
doaj +1 more source
Ridge Regression and Ill-Conditioning [PDF]
, 2014 Hoerl and Kennard (1970) suggested the ridge regression estimator as an alternative to the Ordinary Least Squares (OLS) estimator in the presence of multicollinearity.
Iguernane, Mohamed, Khalaf, Ghadban
core +2 more sources
Modified jackknife ridge estimator for the Conway-Maxwell-Poisson model
Scientific African, 2023 Recently, research papers have shown a strong interest in modeling count data. The over-dispersion or under-dispersion are frequently seen in the count data.
Zakariya Yahya Algamal +3 more
doaj +1 more source
New estimators in a partial linear model depending on an unbiased ridge regression estimator [PDF]
EPJ Web of ConferencesThis paper introduces two new estimators based on the philosophy of unbiased ridge regression estimation, where the parameters are part of a partial linear model suffering from multicollinearity.
Al-Khazraji Yousif A. +1 more
doaj +1 more source
A new hybrid estimator for linear regression model analysis: Computations and simulations
Scientific African, 2023 The Linear regression model explores the relationship between a response variable and one or more independent variables. The parameters in the model are often estimated using the Ordinary Least Square Estimator (OLSE).
G.A. Shewa, F.I. Ugwuowo
doaj +1 more source
Ridge regression revisited [PDF]
, 2005 We argue in this paper that general ridge (GR) regression implies no major complication compared with simple ridge regression. We introduce a generalization of an explicit GR estimator derived by Hemmerle and by Teekens and de Boer and show that this ...
Boer, P.M.C. (Paul) de +1 more
core +1 more source
A Comparison between Biased and Unbiased Estimators in Ordinary Least Squares Regression [PDF]
, 2013 During the past years, different kinds of estimators have been proposed as alternatives to the Ordinary Least Squares (OLS) estimator for the estimation of the regression coefficients in the presence of multicollinearity. In the general linear regression
Khalaf, Ghadban
core +2 more sources
Ancestral State Estimation with Phylogenetic Ridge Regression
Evolutionary Biology, 2020 The inclusion of fossil phenotypes as ancestral character values at nodes in phylogenetic trees is known to increase both the power and reliability of phylogenetic comparative methods (PCMs) applications. We implemented the R function RRphylo as to integrate fossil phenotypic information as ancestral character values.
Silvia Castiglione +8 more
openaire +4 more sources