Ridge and related estimation procedures: theory and practice

For the classical linear regression problem, a number of estimators alternative to least squares have been proposed for situations in which multicollinearity is a problem. There is, however, relatively little known about how these estimators behave in practice. This paper investigates mean square error properties for a number of biased regression estimators, and discusses some practical implications of the use of such estimators, A conclusion is that certain types of ridge estimatorsappear to have good mean square error properties, and this may be useful in situations in which mean square error is important