Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/2180

 Title: Dimension Reduction of the Explanatory Variables in Multiple Linear Regression Authors: Filzmoser, P.Croux, C. Keywords: Multiple Linear RegressionPrincipal Component RegressionDimension ReductionRobustnessRobust 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. Description: 2002 Mathematics Subject Classification: 62J05, 62G35. URI: http://hdl.handle.net/10525/2180 ISSN: 0204-9805 Appears in Collections: 2003 Volume 14

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