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Two‐Group Classification when Both Groups are Mixtures of Normals
Author(s) -
Rawlings R. R.,
Graubard B. I.,
Rae D. S.,
Eckardt M. J.,
Ryback R. S.
Publication year - 1984
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.4710260815
Subject(s) - kernel fisher discriminant analysis , linear discriminant analysis , discriminant , mathematics , discriminant function analysis , optimal discriminant analysis , kernel (algebra) , quadratic equation , quadratic classifier , statistics , pattern recognition (psychology) , group (periodic table) , covariance , quadratic function , function (biology) , artificial intelligence , combinatorics , computer science , biology , support vector machine , chemistry , geometry , organic chemistry , facial recognition system , evolutionary biology
In this paper we consider a two‐group discriminant analysis problem where each group is a mixture of two subgroups. Based upon data from a clinical study of alcohol involvement and diseases, simulation experiments were performed for three different configurations of means and covariance matrices. Expected actual non‐error rates are estimated for the linear, quadratic, and kernel discriminant functions for sample sizes 30, 50, 75, 100, 150 and 200. A conclusion of the article is that the kernel discriminant function performs as well as or better than quadratic discriminant function. However, the linear discriminant function was clearly inferior to either the quadratic or kernel discriminant functions.