[Collinearity and Least Squares Regression]: Comment: Well-Conditioned Collinearity Indices

Collinearity and Least Squares Regression

abstract In this paper we introduce certain numbers, called collinearity indices, which are useful in detecting near collinearities in regression problems. The coeecients enter adversely into formulas concerning signiicance testing and the eeects of errors in the regression variables. Thus they provide simple regression diagnostics, suitable for incorporation in regression packages.

A Projection Algorithm for Regression with Collinearity

Principal component regression (PCR) is often used in regression with multicollinearity. Although this method avoids the problems which can arise in the least squares (LS) approach, it is not optimized with respect to the ability to predict the response variable. We propose a method which combines the two steps in the PCR procedure, namely finding the principal components (PCs) and regression o...

Detects and collinearity in regression model‎ using information theory

‎In this paper‎, ‎collinearity in regression models is introduced and then the procedures on how to‎ " ‎remove it‎" ‎are studied‎. ‎Moreover preliminary definitions have been given‎. ‎And the end of this paper‎, ‎collinearity in regression model will be recognition and a solution will be introduced for remove it‎.   

Understanding the collinearity problem in regression and discriminant analysis

This paper presents a discussion of the collinearity problem in regression and discriminant analysis. The paper describes reasons why the collinearity is a problem for the prediction ability and classification ability of the classical methods. The discussion is based on established formulae for prediction errors. Special emphasis is put on differences and similarities between regression and cla...

Collinearity, Power, and Interpretation of Multiple Regression Analysis