Localized regression

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Discussion Paper ; No. 378

Classification and Topics

• Subject: Local logistic regression ; discrimination ; data adaptive tuning parameters ; selection of predictors ; localized discrimination

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Tutz, Gerhard ; Binder, Harald
• Event: Veröffentlichung
• (who): Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen
• (where): München
• (when): 2004

Further information

• DOI: doi:10.5282/ubm/epub.1749
• Handle: http://hdl.handle.net/10419/31000
• URN: urn:nbn:de:bvb:19-epub-1749-8
• Last update: 10.03.2025, 11:44 AM CET