Universität zu Köln

Pokotylo, Oleksii, Mozharovskyi, Pavlo and Dyckerhoff, Rainer (2019). Depth and Depth-Based Classification with R Package ddalpha. J. Stat. Softw., 91 (5). LOS ANGELES: JOURNAL STATISTICAL SOFTWARE. ISSN 1548-7660

Full text not available from this repository.

Abstract

Following the seminal idea of Tukey (1975), data depth is a function that measures how close an arbitrary point of the space is located to an implicitly defined center of a data cloud. Having undergone theoretical and computational developments, it is now employed in numerous applications with classification being the most popular one. The R package ddalpha is a software directed to fuse experience of the applicant with recent achievements in the area of data depth and depth-based classification. ddalpha provides an implementation for exact and approximate computation of most reasonable and widely applied notions of data depth. These can be further used in the depth-based multivariate and functional classifiers implemented in the package, where the DD alpha-procedure is in the main focus. The package is expandable with user-defined custom depth methods and separators. The implemented functions for depth visualization and the built-in benchmark procedures may also serve to provide insights into the geometry of the data and the quality of pattern recognition.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Pokotylo, Oleksii UNSPECIFIED UNSPECIFIED UNSPECIFIED Mozharovskyi, Pavlo UNSPECIFIED UNSPECIFIED UNSPECIFIED Dyckerhoff, Rainer UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-132065
DOI:	10.18637/jss.v091.i05
Journal or Publication Title:	J. Stat. Softw.
Volume:	91
Number:	5
Date:	2019
Publisher:	JOURNAL STATISTICAL SOFTWARE
Place of Publication:	LOS ANGELES
ISSN:	1548-7660
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language HALF-SPACE DEPTH; FUNCTIONAL DATA; MULTIVARIATE; REGRESSION; ALGORITHM; NOTION Multiple languages Computer Science, Interdisciplinary Applications; Statistics & Probability Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/13206