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Generalized Walker‐Duncan Procedure for Obtaining Maximum Likelihood Estimates of Parameters of Logistic Discriminant Function
Author(s) -
Keusińska Ewa
Publication year - 1988
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.4710300302
Subject(s) - discriminant function analysis , linear discriminant analysis , mathematics , statistics , logistic regression , discriminant , quadratic equation , logistic function , generalization , optimal discriminant analysis , quadratic classifier , quadratic function , function (biology) , likelihood function , pattern recognition (psychology) , estimation theory , artificial intelligence , computer science , support vector machine , mathematical analysis , geometry , evolutionary biology , biology
A recursive method of obtaining the maximum likelihood estimates of the parameters of the quadratic logistic discriminant function is presented. This method is an extension of the Walker and Duncan procedure (1967) proposed for the linear logistic discriminant function in a dichotomous case. A generalization of the method to the problem of discrimination between several populations is also given in the paper. It works for both linear and quadratic logistic discriminant function. After an estimation of the parameters of the logistic function a classification can be performed. An example of application of the method to automatic diagnosis of some respiratory diseases is presented. Comparison with the standard procedures used for the estimation is done by a short simulation study.