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Minimum Distance Probability Discriminant Analysis for Mixed Variables
Author(s) -
Núñez Marian,
Villarroya Angel,
Oller José María
Publication year - 2003
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/1541-0420.00031
Subject(s) - categorical variable , mathematics , statistical distance , linear discriminant analysis , discriminant function analysis , statistics , discriminant , parametric statistics , probability distribution , computer science , artificial intelligence
Summary Minimum distance probability (MDP) is a robust discriminant algorithm based on a distance function. In this article, we generalize the use of MDP to the case of mixed (continuous and categorical) variables by means of the individual‐score (IS) distance. This distance assumes an underlying parametric model and is based on the score transformation of the data. We have adapted it to the usual case of ignoring the distribution of the whole set of observed variables, but assuming that some knowledge about the marginal distributions is available. Finally, MDP with IS distance (IS‐MDP) is compared with other discriminant methods (including those designed for mixed data) in several examples and simulations. IS‐MDP is shown to be the most efficient method according the leave‐one‐out criterion.