DIMACS Workshop on Data Depth: Robust Multivariate Analysis, Computational Geometry and Applications
May 14 - 16, 2003
DIMACS Center, Rutgers University, Piscataway, NJ
- Organizers:
- 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.
STUDENT SCHOLARSHIPS:
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.