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The Shrinkage of Point Scoring Methods
Author(s) -
Copas J. B.
Publication year - 1993
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.2307/2986235
Subject(s) - shrinkage , point (geometry) , computer science , mathematics , machine learning , geometry
SUMMARY Point scoring, widely used in criminology and other social sciences, is a simple way of predicting a binary response on the basis of binary explanatory variables. Like all statistical predictors they are liable to shrinkage, working less well on a validation sample than they appear to do on the original data. The paper examines the extent of shrinkage and proposes shrinkage‐adjusted predictions. The related 'independence Bayes' method is also considered, and found to shrink more than the basic point scoring method. The results are applied to data from a cohort study in the development of delinquency.