Efficient algorithms for maximum regression depth

Marc Van Kreveld*, Joseph S.B. Mitchell, Peter Rousseeuw, Micha Sharir, Jack Snoeyink, Bettina Speckmann

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

We investigate algorithmic questions that arise in the statistical problem of computing lines or hyperplanes of maximum regression depth among a set of n points. We work primarily with a dual representation and find points of maximum undirected depth in an arrangement of lines or hyperplanes. An O(n d ) time and O(n d-1) space algorithm computes undirected depth of all points in d dimensions. Properties of undirected depth lead to an O(nlog 2 n) time and O(n) space algorithm for computing a point of maximum depth in two dimensions, which has been improved to an O(nlog n) time algorithm by Langerman and Steiger (Discrete Comput. Geom. 30(2):299-309, [2003]). Furthermore, we describe the structure of depth in the plane and higher dimensions, leading to various other geometric and algorithmic results.

Original languageEnglish
Pages (from-to)656-677
Number of pages22
JournalDiscrete and Computational Geometry
Volume39
Issue number4
DOIs
StatePublished - Jun 2008

Funding

FundersFunder number
ESPRIT IV LTR21957
G.I.F.
Killam Foundation
National Science Foundation9732220, CCR-94-24398, 9504192, CCR-9504192, CCR-97-32101, CCR-9732220
BOEING
Council for International Exchange of Scholars
Natural Sciences and Engineering Research Council of Canada
German-Israeli Foundation for Scientific Research and Development
United States-Israel Binational Science Foundation
Nederlandse Organisatie voor Wetenschappelijk Onderzoek642.065.503
Tel Aviv University
University of British Columbia

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