On maximum depth classifiers: depth distribution approach

Olusola Samuel Makinde and Olusoga Akin Fasoranbaku

Journal of Applied Statistics, 2018, vol. 45, issue 6, 1106-1117

Abstract: In this paper, we consider the notions of data depth for ordering multivariate data and propose a classification rule based on the distribution of some depth functions in $ \mathbb {R}^d $ Rd. The equivalence of the proposed classification rule to optimal Bayes rule is discussed under suitable conditions. The performance of the proposed classification method is investigated in low- and high-dimensional setting using real datasets. Also, the performance of the proposed classification method is illustrated in comparison to some other depth-based classifiers using simulated data sets.

Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/02664763.2017.1342783 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:45:y:2018:i:6:p:1106-1117

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664763.2017.1342783

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().