Selection of Variables in Discriminant Analysis.
Abstract
In a number of disciplines, data analysts are confronted with the problem of classifying an observation into one of the distinct groups when the number of variables is very large. So, it is of interest to find out a smaller number of important variables which are adequate for discrimination. These variables may be a subset of the original variables or certain linear combinations of the original variables. The selection of variables is important since there are situations where inclusion of unimportant variables may actually decrease the ability for discrimination. Apart from it, it is more feasible to analyze the data from cost and computational considerations if the number of variables is small. In this chapter, we discuss various procedures for the selection of variables in discriminant analysis. In Section 2, we discuss procedures to find out whether certain discriminant coefficients associated with variables are important for discrimination between two populations. In Section 3, generalizations of the above procedures for several populations are discussed. In Section 4, we discuss various procedures to determine the number of important discriminant functions.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 01, 1982
- Accession Number
- ADA121143
Entities
People
- Paruchuri R. Krishnaiah
Organizations
- University of Pittsburgh