Collinearity Detection in Linear Regression Models.

Multicollinearity can seriously affect least-squares parameter estimates. Many methods have been suggested to determine those parameters most involved. This paper, beginning with the contributions of Belsley, Kuh, and Welsch (1980) and Belsley (1991), forges a new direction. A decomposition of the variable space allows the near dependencies to be isolated in one subspace. And this in turn allows a corresponding decomposition of the main statistics, as well as a new one proposed here, to provide better information on the structure of the collinear relations. Citation Copyright 1996 by Kluwer Academic Publishers.