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Depth-Based Classification for Distributions with Nonconvex Support
Joint Authors
Hlubinka, Daniel
Vencalek, Ondrej
Source
Journal of Probability and Statistics
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-09-22
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
Halfspace depth became a popular nonparametric tool for statistical analysis of multivariate data during the last two decades.
One of applications of data depth considered recently in literature is the classification problem.
The data depth approach is used instead of the linear discriminant analysis mostly to avoid the parametric assumptions and to get better classifier for data whose distribution is not elliptically symmetric, for example, skewed data.
In our paper, we suggest to use weighted version of halfspace depth rather than the halfspace depth itself in order to obtain lower misclassification rate in the case of “nonconvex” distributions.
Simulations show that the results of depth-based classifiers are comparable with linear discriminant analysis for two normal populations, while for nonelliptic distributions the classifier based on weighted halfspace depth outperforms both linear discriminant analysis and classifier based on the usual (nonweighted) halfspace depth.
American Psychological Association (APA)
Hlubinka, Daniel& Vencalek, Ondrej. 2013. Depth-Based Classification for Distributions with Nonconvex Support. Journal of Probability and Statistics،Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-486457
Modern Language Association (MLA)
Hlubinka, Daniel& Vencalek, Ondrej. Depth-Based Classification for Distributions with Nonconvex Support. Journal of Probability and Statistics No. 2013 (2013), pp.1-7.
https://search.emarefa.net/detail/BIM-486457
American Medical Association (AMA)
Hlubinka, Daniel& Vencalek, Ondrej. Depth-Based Classification for Distributions with Nonconvex Support. Journal of Probability and Statistics. 2013. Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-486457
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-486457