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:: Volume 17, Issue 2 (2-2024) ::
JSS 2024, 17(2): 0-0 Back to browse issues page
Multi-class Depth-based Classification for Multivariate Data
Sara Bayat , Sakineh Dehghan *
Abstract:   (1044 Views)

‎This paper presents a nonparametric multi-class depth-based classification approach for multivariate data. This approach is easy to implement rather than most existing nonparametric methods that have computational complexity. If the assumption of the elliptical symmetry holds, this method is equivalent to the Bayes optimal rule. Some simulated data sets as well as real example have been used to evaluate the performance of these depth-based classifiers.

Keywords: Depth function, Classification‎‎, Bayes optimal rule, ‎Elliptical symmetry
Full-Text [PDF 304 kb]   (511 Downloads)    
Type of Study: Applied | Subject: Applied Statistics
Received: 2023/01/14 | Accepted: 2024/02/29 | Published: 2024/02/22
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Bayat S, Dehghan S. Multi-class Depth-based Classification for Multivariate Data. JSS 2024; 17 (2)
URL: http://jss.irstat.ir/article-1-832-en.html

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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 17, Issue 2 (2-2024) Back to browse issues page
مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences

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