Robust Learning from Bites

dc.contributor.author	Christmann, Andreas	de
dc.date.accessioned	2005-03-08T15:23:52Z
dc.date.available	2005-03-08T15:23:52Z
dc.date.issued	2005	de
dc.format.extent	231905 bytes
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/2003/20156
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-6210
dc.language.iso	en	de
dc.publisher	Universität Dortmund	de
dc.subject	Many robust statistical procedures have two drawbacks. Firstly, they are computer-intensive such that they can hardly be used for massive data sets. Secondly, robust confidence intervals for the estimated parameters or robust predictions according to the fitted models are often unknown. Here, we propose a general method to overcome these problems of robust estimation in the context of huge data sets. The method is scalable to the memory of the computer, can be distributed on several processors if available, and can help to reduce the computation time substantially. The method additionally offers distribution-free confidence intervals for the median of the predictions. The method is illustrated for two situations: robust estimation in linear regression and kernel logistic regression from statistical machine learning.	en
dc.subject.ddc	310	de
dc.title	Robust Learning from Bites	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access

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