Tutz, G. and Binder, H. (2005) Localized classification. STATISTICS AND COMPUTING, 15 (3). pp. 155-166. ISSN 0960-3174,
Full text not available from this repository. (Request a copy)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. However, if localization is combined with a reduction of dimension the initial number of variables is less restricted. 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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | LOGISTIC-REGRESSION; MODELS; APPROXIMATION; local logistic regression; discrimination; data adaptive tuning parameters; selection of predictors; localized discrimination |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Psychiatrie und Psychotherapie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 10 May 2021 07:01 |
| Last Modified: | 10 May 2021 07:01 |
| URI: | https://pred.uni-regensburg.de/id/eprint/35976 |
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