The robust beauty of improper linear models in decision making.
Abstract
Proper linear models are those in which predictor variables are given weights in such a way that the resulting linear composite optimally predicts some criterion of interest; examples of proper linear models are standard regression analysis, discriminant function analysis, and ridge regression analysis. Research summarized in Paul Meehl's book on clinical versus statistical prediction—and a plethora of research stimulated in part by that...
Paper Details
Title
The robust beauty of improper linear models in decision making.
Published Date
Jul 1, 1979
Journal
Volume
34
Issue
7
Pages
571 - 582
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Notes
History