Results 11 to 20 of about 835 (216)
A New Type Iterative Ridge Estimator: Applications and Performance Evaluations
Journal of Mathematics, 2022 The usage of the ridge estimators is very common in presence of multicollinearity in multiple linear regression models. The ridge estimators are used as an alternative to ordinary least squares in case of multicollinearity as they have lower mean square ...
Aydın Karakoca
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Inference in Linear Models with Nonstochastic Biased Factors [PDF]
The Egyptian Statistical Journal, 1996 Obenchain (1977) claimed that ridge techniques with nonstochastic of biased factors don't generally yield "new" normal theory statistical inference than that used in least squares technique, and that the t and F statistics are identical under both ...
Abdul-Mordy Azzam
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Robust weighted ridge regression based on S – estimator
African Scientific Reports, 2023 Ordinary least squares (OLS) estimator performance is seriously threatened by correlated regressors often called multicollinearity. Multicollinearity is a situation when there is strong relationship between any two exogenous variables.
Taiwo Stephen Fayose +3 more
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Journal of Economics and Administrative Sciences, 2019 Maulticollinearity is a problem that always occurs when two or more predictor variables are correlated with each other. consist of the breach of one basic assumptions of the ordinary least squares method with biased estimates results, There are several methods which are proposed to handle this problem including the method To address a problem and ...
Emad Hazim Aboudi, Ahmed Jabaar Hmood
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A New Convex Estimator Combining Ridge and Ordinary Least Squares Estimators [PDF]
مجلة جامعة الانبار للعلوم الصرفةIn the presence of high correlation between the independent variables in the linear regression model, which is known as the multicollinearity problem, the ordinary least squares estimator produce large variations in the sample.
Karam Al-janabi, Mustafa Alheety
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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
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Ridge Shrinkage Estimators in Finite Mixture of Generalized Estimating Equations. [PDF]
Mathematics and Modeling in Finance, 2022 The paper considers the problem of estimation of the parameters in nite mixture models.In this article, a new method is proposed for of estimation of the parameters in nite mixture models. Traditionally, the parameter estimation in nite mixture models
Sajad Nezamdoust, Farzad Eskandari
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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
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Ibn Al-Haitham Journal for Pure and Applied Sciences, 2018 In the presence of multi-collinearity problem, the parameter estimation method based on the ordinary least squares procedure is unsatisfactory. In 1970, Hoerl and Kennard insert analternative method labeled as estimator of ridge regression.
Hazim Mansoor Gorgees +1 more
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The performance of some new estimated ridge parameter regression model [PDF]
مجلة جامعة الانبار للعلوم الصرفةIn the presence of high correlation between the independent variables in the linear regression model, which is known as the multicollinearity problem, the ordinary least squares estimator produces large variations in the sample. To overcome this problem,
Fatima ALfahdawe, Mustafa Alheety
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