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Discriminant analysis using the unweighted sum of binary variables: a comparison of model selection methods

✍ Scribed by Douglas R. Langbehn; Robert F. Woolson

Publisher: John Wiley and Sons
Year: 1997
Tongue: English
Weight: 731 KB
Volume: 16
Category: Article
ISSN: 0277-6715
DOI: 10.1002/(sici)1097-0258(19971215)16:23<2679::aid-sim695>3.0.co;2-1

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✦ Synopsis

Many clinical decision-making rules are equivalent to linear discriminant functions that involve the unweighted sum of binary variables (SBV). We briefly consider the geometry of this restriction and then propose a number of methods for forward stepwise selection of SBV models. Using a simulation study, we compare the performance of these methods under a wide range of plausible conditions and show that no single method is uniformly superior for selecting models of a fixed size. Factors of general importance in relative method performance are the ratio of sample size to the number of candidate variables and the class-conditional moment structure of the data. We conclude by offering some practical strategies for SBV model construction.

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