Shape analysis in the light of simplicial depth estimators

KATINA, Stanislav. Shape analysis in the light of simplicial depth estimators. Online. In Mardia K.V. Systems Biology & Statistical Bioinformatics. 1st ed. Leeds: The University of Leeds, 2010, p. 51-54. ISBN 978-0-85316-263-6.

Basic information

• Original name: Shape analysis in the light of simplicial depth estimators
• Authors: KATINA, Stanislav (703 Slovakia, guarantor, belonging to the institution).
• Edition: 1. vyd. Leeds, Systems Biology & Statistical Bioinformatics, p. 51-54, 4 pp. 2010.
• Publisher: The University of Leeds

Other information

• Original language: English
• Type of outcome: Proceedings paper
• Field of Study: 10103 Statistics and probability
• Country of publisher: United Kingdom of Great Britain and Northern Ireland
• Confidentiality degree: is not subject to a state or trade secret
• Publication form: electronic version available online
• WWW: URL
• RIV identification code: RIV/00216224:14310/10:00063988
• Organization unit: Faculty of Science
• ISBN: 978-0-85316-263-6
• Keywords in English: simplicial depth; shape analysis
• Tags: AKR, rivok

Abstract

In this paper we present the maximum simplicial depth estimator and compare it to the ordinary least square estimator in examples from 2D shape analysis focusing on bivariate and multivariate allometrical problems from zoology. We compare two types of estimators derived under different subsets of parametric space on the basis of the linear regression model. In applications where outliers in the x- or y-axis direction occur in the data and residuals from ordinary least-square (OLS) linear regression model are not normally distributed, we recommend the use of the maximum simplicial depth estimators.

Links

• CZ.1.07/2.2.00/15.0203, interní kód MU: Name: Univerzitní výuka matematiky v měnícím se světě (Acronym: Univerzitní výuka matematiky)

Investor: Ministry of Education, Youth and Sports of the CR, 2.2 Higher education