Discriminant Analysis with Highly Intercorrelated Variables.
WYOMING UNIV LARAMIE STATISTICS LAB
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Discriminant analysis is considered for populations possessing multivariate normal distributions with possibly different means but a common variance-covariance matrix sigma. The form of the discriminant scores is derived for the case when sigma is singular and is used to justify a procedure for selection of meaningful discriminant scores when the estimate of sigma is near singular i.e., when the estimate of sigma has some small characteristic roots. The procedure is based entirely on sample data and for appropriate cases will yield the usual discriminant scores. The primary application is to situations where the variables are highly intercorrelated i.e., for multicolinear variables. For such cases, substantial improvement in the misclassification rates is sometimes possible. The procedure has additional applications illustrated herein and can be easily incorporated into a computer program for routine data analysis.
- Statistics and Probability