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Data Depth: Robust Multivariate Analysis, Computational Geometry and Applications
Edited by: Regina Y. Liu, Rutgers University, New Brunswick, NJ, Robert Serfling, University of Texas at Dallas, Richardson, TX, and Diane L. Souvaine, Tufts University, Medford, MA
A co-publication of the AMS and DIMACS.

DIMACS: Series in Discrete Mathematics and Theoretical Computer Science
2006; 246 pp; hardcover
Volume: 72
ISBN-10: 0-8218-3596-3
ISBN-13: 978-0-8218-3596-8
List Price: US$97
Member Price: US$77.60
Order Code: DIMACS/72
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The book is a collection of some of the research presented at the workshop of the same name held in May 2003 at Rutgers University. The workshop brought together researchers from two different communities: statisticians and specialists in computational geometry. The main idea unifying these two research areas turned out to be the notion of data depth, which is an important notion both in statistics and in the study of efficiency of algorithms used in computational geometry. Many of the articles in the book lay down the foundations for further collaboration and interdisciplinary research.

Co-published with the Center for Discrete Mathematics and Theoretical Computer Science beginning with Volume 8. Volumes 1-7 were co-published with the Association for Computer Machinery (ACM).


Graduate students and research mathematicians interested in multivariate analysis and computational geometry.


"This book clearly satisfies the goals of the editors: it contains state-of-the-art contributions on data depth that may be of interest to statisticians, mathematicians, computer scientists, and computational geometers. Connections between the different research fields are well exposed. This will certainly stimulate further interdisciplinary research."

-- Biometrics

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