DIMACS Workshop on Data Depth: Robust Multivariate Analysis, Computational Geometry and Applications

May 14 - 16, 2003
DIMACS Center, Rutgers University, Piscataway, NJ

Regina Liu, Rutgers University, rliu@stat.rutgers.edu
Robert Serfling, University of Texas at Dallas, serfling@utdallas.edu
Diane Souvaine, Tufts University, dls@eecs.tufts.edu
Yehuda Vardi, Rutgers University, vardi@stat.rutgers.edu
Presented under the auspices of the DIMACS Special Focus on Data Analysis and Mining and the DIMACS Special Focus on Computational Geometry and Applications.

This material is based upon work supported by the National Science Foundation under Grant No. 0205136.

Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF).

Multivariate statistical methodology plays a role of ever increasing importance in real life applications, which typically entail a host of interrelated variables. Simple extensions of univariate statistics to the multivariate setting do not properly capture the higher-dimensional features of multivariate data, nor do they yield geometric solutions because of the absence of a natural order for multidimensional Euclidean space. A more promising approach is the one based on "data depth", which can provide a center-outward ordering of points in Euclidean space of any dimension. Extensive developments in recent years have generated many attractive depth-based tools for multivariate data analysis, with a wide range of applications. The diversity in approaches, emphases, and concepts, however, makes it necessary to seek unified views and perspectives that would guide the further development of the depth-based approach.

The concept of data depth provides new perspectives to probabilistic as well as computational geometries. In particular, the development of implementable computing algorithms for depth-based statistics has brought about many new challenges in computational geometry. This workshop would create a unique environment for multidisciplinary collaboration among computer scientists, theoretical and applied statisticians, and data analysts. It would bring together active researchers in these fields to discuss significant open issues, establish perspective on applications, and set directions for further research.


The workshop offers some student scholarships to reimburse expenses for travel, registration, and lodging at the workshop, up to $400. Applicants for student scholarships should contact Regina Liu (rliu@stat.rutgers.edu).

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Document last modified on December 23, 2003.