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Linear Discriminant Functions for Stationary Time Series
Author(s) -
Krzyśko Mirosłw,
Wołński Waldemar
Publication year - 1997
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.4710390807
Subject(s) - linear discriminant analysis , mathematics , discriminant , optimal discriminant analysis , discriminant function analysis , series (stratigraphy) , class (philosophy) , measure (data warehouse) , multiple discriminant analysis , function (biology) , pattern recognition (psychology) , statistics , artificial intelligence , computer science , data mining , paleontology , evolutionary biology , biology
Optimal classification rules based on linear functions which maximize the Chernoff distance, or the Morisita distance, or the Kullback‐Leibler distance are studied here. We obtain an expression for the optimal linear discriminant function and show that the resulting linear procedure belongs to the Anderson‐Bahadur admissible class. For the comparison of discriminant rules we use some index which is the measure of the accuracy of a given class of discriminant procedures. The asymptotic form of the discriminant function is also studied.