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

Published

Journal Article (Review)

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.

Full Text

Cited Authors

  • Langbehn, DR; Woolson, RF

Published Date

  • December 1997

Published In

Volume / Issue

  • 16 / 23

Start / End Page

  • 2679 - 2700

PubMed ID

  • 9421869

Pubmed Central ID

  • 9421869

Electronic International Standard Serial Number (EISSN)

  • 1097-0258

International Standard Serial Number (ISSN)

  • 0277-6715

Digital Object Identifier (DOI)

  • 10.1002/(sici)1097-0258(19971215)16:23<2679::aid-sim695>3.0.co;2-1

Language

  • eng