Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/31000
Authors: 
Tutz, Gerhard
Binder, Harald
Year of Publication: 
2004
Series/Report no.: 
Discussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 378
Abstract: 
The main problem with localized discriminant techniques is the curse of dimensionality, which seems to restrict their use to the case of few variables. This restriction does not hold if localization is combined with a reduction of dimension. In particular it is shown that localization yields powerful classifiers even in higher dimensions if localization is combined with locally adaptive selection of predictors. A robust localized logistic regression (LLR) method is developed for which all tuning parameters are chosen data-adaptively. In an extended simulation study we evaluate the potential of the proposed procedure for various types of data and compare it to other classification procedures. In addition we demonstrate that automatic choice of localization, predictor selection and penalty parameters based on cross validation is working well. Finally the method is applied to real data sets and its real world performance is compared to alternative procedures.
Subjects: 
Local logistic regression
discrimination
data adaptive tuning parameters
selection of predictors
localized discrimination
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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