title: Discriminant analysis using the unweighted sum of binary variables: a comparison of model selection methods.
abstract: 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.
authors.affiliation: Department of Psychiatry, University of Iowa, Iowa City 52242, USA.
authors.forename: D R
authors.initials: DR
authors.lastname: Langbehn
authors.forename: R F
authors.initials: RF
authors.lastname: Woolson
mesh_terms: D003198:Computer Simulation; D003661:Decision Support Techniques; D016002:Discriminant Analysis; D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method; D012044:Regression Analysis; D018709:Statistics, Nonparametric