Premium
On the Performance of Some Classification Rules for Qualitative Data for Simulated Underlying Distributions
Author(s) -
Trampisch H. J.
Publication year - 1983
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.19830250708
Subject(s) - multinomial distribution , mathematics , linear discriminant analysis , discriminant function analysis , independence (probability theory) , monte carlo method , quadratic equation , function (biology) , classification rule , statistics , algorithm , artificial intelligence , computer science , geometry , evolutionary biology , biology
Two linear functions for discriminating with qualitative variables (Fisher's linear discriminant function and the independence rule) are compared with the general multinomial procedure, a rule based on Lancaster's definition of higher order interactions and the quadratic discriminant function. The evaluation of these functions is carried out within Monte Carlo experiments. Various types of underlying distributions generated by a special algorithm are used.