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New Perspectives in Stochastic Geometry$
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Wilfrid S. Kendall and Ilya Molchanov

Print publication date: 2009

Print ISBN-13: 9780199232574

Published to Oxford Scholarship Online: February 2010

DOI: 10.1093/acprof:oso/9780199232574.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 17 June 2021

Data Depth: Multivariate Statistics and Geometry

Data Depth: Multivariate Statistics and Geometry

Chapter:
(p.398) 12 Data Depth: Multivariate Statistics and Geometry
Source:
New Perspectives in Stochastic Geometry
Author(s):

Cascos Ignacio

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199232574.003.0012

This chapter presents several ways to measure the degree of centrality of a point with respect to a multivariate probability distribution or a data cloud. Such degree of centrality is called depth, and it can be used to extend a wide range of univariate techniques that are based on the natural order on the real line to the multivariate setting.

Keywords:   multivariate probability distribution, data cloud, depth, univariate techniques

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