Comparison of algorithms for replacing missing data in discriminant analysis

Communications in Statistics - Theory and Methods
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Abstract

We examined the impact of different methods for replacing missing data in discriminant analyses conducted on randomly generated samples from multivariate normal and non-normal distributions. The probabilities of correct classification were obtained for these discriminant analyses before and after randomly deleting data as well as after deleted data were replaced using: (1) variable means, (2) principal component projections, and (3) the EM algorithm. Populations compared were: (1) multivariate normal with covariance matrices ∑1=∑2, (2) multivariate normal with ∑1≠∑2 and (3) multivariate non-normal with ∑1=∑2. Differences in the probabilities of correct classification were most evident for populations with small Mahalanobis distances or high proportions of missing data. The three replacement methods performed similarly but all were better than non - replacement.

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title Comparison of algorithms for replacing missing data in discriminant analysis
Series title Communications in Statistics - Theory and Methods
DOI 10.1080/03610929208830864
Volume 21
Issue 6
Year Published 1992
Language English
Publisher Taylor & Francis
Contributing office(s) National Wetlands Research Center
Description 12 p.
First page 1567
Last page 1578