Multiple Linear Regression Principal Component Regression Dimension Reduction Robustness Robust Regression
Issue Date:
2003
Publisher:
Institute of Mathematics and Informatics Bulgarian Academy of Sciences
Citation:
Pliska Studia Mathematica Bulgarica, Vol. 14, No 1, (2003), 59p-70p
Abstract:
In classical multiple linear regression analysis problems will occur if the
regressors are either multicollinear or if the number of regressors is larger
than the number of observations. In this note a new method is introduced
which constructs orthogonal predictor variables in a way to have a maximal
correlation with the dependent variable. The predictor variables are linear
combinations of the original regressors. This method allows a major reduction of the number of predictors in the model, compared to other standard methods like principal component regression. Its computation is simple and quite fast. Moreover, it can easily be robustified using a robust regression
technique and a robust measure of correlation.