In our work, we explored multicollinearity problem from a complex point of view - from diagnostic methods to the solving of the problems which are caused by the multicollinearity. We compared the Least Squares method with some alternative methods - Principal Component Regression, Partial Least Squares Regression and Ridge Regression on the theoretical basis. In the last section, we demonstrated all methods on practical example computed in the program R
The aim of this study is to compare some regression methods in the presence of multicollinearity pro...
Regression analysis is an analysis used to determine the effect between the independent variable an...
The presence of multicollinearity can induce large variances in the ordinary Least-squares estimates...
In our work, we explored multicollinearity problem from a complex point of view - from diagnostic me...
Abstract In regression, the objective is to explain the variation in one or more response variables,...
In regression, the objective is to explain the variation in one or more response variables, by assoc...
Multicollinearity is one of several problems confronting researchers using regression analysis. This...
Multicollinearity is one of the most important issues in regression analysis, as it produces unstabl...
Abstract: If there is no linear relationship between the regressors, they are said to be orthogonal....
Multicollinearity has remained a major problem in regression analysis and should be sustainably addr...
Ridge regression is an alternative to ordinary least-squares (OLS) regression. It is believed to be ...
<p class="AbstractText">Multicollinearity is a problem that often occurs in multiple linear regressi...
We present and compare principal components regression and partial least squares regression, and the...
In multiple linear regression analysis, linear dependencies in the regressor variables lead to ill-...
When the multicollinearity among the independent variables in a regression model is due to the high ...
The aim of this study is to compare some regression methods in the presence of multicollinearity pro...
Regression analysis is an analysis used to determine the effect between the independent variable an...
The presence of multicollinearity can induce large variances in the ordinary Least-squares estimates...
In our work, we explored multicollinearity problem from a complex point of view - from diagnostic me...
Abstract In regression, the objective is to explain the variation in one or more response variables,...
In regression, the objective is to explain the variation in one or more response variables, by assoc...
Multicollinearity is one of several problems confronting researchers using regression analysis. This...
Multicollinearity is one of the most important issues in regression analysis, as it produces unstabl...
Abstract: If there is no linear relationship between the regressors, they are said to be orthogonal....
Multicollinearity has remained a major problem in regression analysis and should be sustainably addr...
Ridge regression is an alternative to ordinary least-squares (OLS) regression. It is believed to be ...
<p class="AbstractText">Multicollinearity is a problem that often occurs in multiple linear regressi...
We present and compare principal components regression and partial least squares regression, and the...
In multiple linear regression analysis, linear dependencies in the regressor variables lead to ill-...
When the multicollinearity among the independent variables in a regression model is due to the high ...
The aim of this study is to compare some regression methods in the presence of multicollinearity pro...
Regression analysis is an analysis used to determine the effect between the independent variable an...
The presence of multicollinearity can induce large variances in the ordinary Least-squares estimates...