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MULTIPLE DISCRIMINANT ANALYSIS AND SEVERAL SMALL SAMPLES‐TO SPLIT, COMBINE, OR TREAT SEPARATELY
Author(s) -
Drevs Robert A.,
Durand Richard M.,
Mattheiss T. H.
Publication year - 1977
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1977.tb01103.x
Subject(s) - linear discriminant analysis , discriminant , homogeneous , optimal discriminant analysis , computer science , artificial intelligence , machine learning , statistics , mathematics , pattern recognition (psychology) , combinatorics
This paper provides a methodology for investigating whether different groups perceived as being similar can be treated as homogeneous and therefore can be effectively combined for Multiple Discriminant Analysis purposes. The consequences of combining heterogeneous groups are explored.