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

• David Eppstein, University of California
  Robust Statistics and Arrangements

• Belen Fernandez-de-Castro, Universidade de Santiago de Compostela, Spain
  Functional Samples and Bootstrap for Predicting SO 2 Levels

• Sara Lopez-Pintado, Juan Romo, Universidad Carlos III de Madrid, Spain
  A definition of depth for functional observations

• Peter Meer, Rutgers University
  Nonparametric Clustering of High-Dimensional Data

• Karl Mosler, Universitat zu Koln, Germany
  Data Analysis by Zonoid Depth

• Hannu Oja, University of Jyvaskyla, Finland
  On the Geometry of Multivariate L1 Objective Functions

• Eynat Rafalin, Tufts University
  A computational tool for depth-based Statistical analysis

• Mario Romanazzi, Ca' Foscari University of Venice, Italy
  Data Depth in Multivariate Analysis: Dependence, Discrimination and Clustering

• Vera Rosta, McGill University
  Exact, Adaptive, Parallel Algorithms for Data Depth Problems

• Suresh Venkatasubramanian, AT&T Labs - Research
  Real-time Computation of Data Depth Using
  the Graphics Pipeline

• Yijun Zuo, Michigan State University
  Computing of Projection Depth and Related Estimators

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