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Ridge-Type Estimators for Regression Analysis
Journal of the Royal Statistical Society Series B: Statistical Methodology, 1974 Summary An examination of the mean-square error properties of a class of shrinkage estimators for the normal regression model leads to a new derivation of the Hoerl–Kennard (1970) Ridge estimator and its generalization. Comparison is made with the James–Stein estimator, and with the generalized-inverse estimator proposed by Marquardt ...
Goldstein, M., Smith, A. F. M.
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Boosting Ridge Regression [PDF]
, 2005 Ridge regression is a well established method to shrink regression parameters towards zero, thereby securing existence of estimates. The present paper investigates several approaches to combining ridge regression with boosting techniques.
Binder, Harald, Tutz, Gerhard
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Almost unbiased ridge estimator in the count data regression models [PDF]
, 2021 The ridge estimator has been consistently demonstrated to be an attractive shrinkage method to reduce the effects of multicollinearity. The Poisson regression negative binomial regression models are well-known model in application when the response ...
Algamal, Zakariya Yahya; University of Mosul +1 more
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Treating Multicollinearity Problem Using Gool Programming Technique [PDF]
The Egyptian Statistical Journal, 2011 Multiple regression analysis is usually efficient for prediction, but often produces poor results because of the multicollinearity among the independent variables.
Afaf El-Dash +2 more
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F1000Research, 2021 Background: Multicollinearity greatly affects the Maximum Likelihood Estimator (MLE) efficiency in both the linear regression model and the generalized linear model. Alternative estimators to the MLE include the ridge estimator, the Liu estimator and the
Olukayode Adebimpe +4 more
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Comparison of regression models under multi-collinearity [PDF]
, 2018 Multicollinearity is a major problem in linear regression analysis and several methods exists in the literature to deal with the same. Ridge regression is one of the most popular methods to overcome the problem followed by Generalized Ridge Regression ...
Srinivasan, Rangasami M.; Department of Statistics, University of Madras +2 more
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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
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Ordinal Ridge Regression with Categorical Predictors [PDF]
, 2011 In multi-category response models categories are often ordered. In case of ordinal response models, the usual likelihood approach becomes unstable with ill-conditioned predictor space or when the number of parameters to be estimated is large relative to ...
Zahid, Faisal Maqbool
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An improved ridge type estimator for logistic regression [PDF]
, 2022 In this paper, an improved ridge type estimator is introduced to overcome the effect of multicollinearity in logistic regression. The proposed estimator is called a modified almost unbiased ridge logistic estimator.
Varathan, Nagarajah
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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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