Results 21 to 30 of about 2,638,357 (294)
Minimax Ridge Regression Estimation. [PDF]
The Annals of Statistics, 1977 The technique of ridge regression, first proposed by Hoerl and Kennard, has become a popular tool for data analysts faced with a high degree of multicollinearity in their data. By using a ridge estimator, one hopes to both stabilize one's estimates (lower the condition number of the design matrix) and improve upon the squared error loss of the least ...
openaire +2 more sources
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
doaj +1 more source
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
core +1 more source
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
core +1 more source
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
doaj +1 more source
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
doaj +1 more source
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 +3 more sources
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
core +1 more source
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
Ridge Estimation for Multinomial Logit Models with Symmetric Side Constraints [PDF]
, 2009 In multinomial logit models, the identifiability of parameter estimates is typically obtained by side constraints that specify one of the response categories as reference category.
Tutz, Gerhard, Zahid, Faisal Maqbool
core +1 more source