3rd IASC world conference on
Computational Statistics & Data Analysis
Amathus Beach Hotel, Limassol, Cyprus, 28-31 October, 2005
 
Title: Flexible function estimation in high dimensional problems

Description:

Function estimation tasks in high dimensions occur in various areas of statistical application. Such applications are not restricted to science and engineering. They are also relevant for modelling in finance and economics. Most recently high dimensional estimation problems have gained a lot of attention in the context of genetic research (e.g. the statistical analysis of microarray data). An additional complication in these new bioscience applications is the fact that we are confronted with a very large number of variables (e.g. genes) and at the same time a moderate number of observational units (e.g. patients). This situation leads to ill-posed problems resulting in overfitting of models and poor predictive ability. Apart from methodological questions there are numerous numerical and computational challenges which should be in the focus of the CSDA journal (e.g. special issue). As a matter of fact we are not only confronted with potential non-linearity of the functions involved and the curse of dimensionality due to data sparseness in feature space, but also with the requirement of complexity reduction (regularization).

Our intention is to bridge the gap between the most successful research concerning non- and semiparametric techniques which has evolved over the last 30 years and relatively new statistical learning approaches such as support vector machines. They have the ability to control complexity in high dimensional problems at the cost of linearizing functional relationships. The wealth of experience concerning bandwidth choice in nonparametric regression and density estimation could cross-fertilize attempts to select complexity parameters in statistical learning. The ultimate goal of future research would be non- or semiparametric approaches that allow to control complexity without implicit restrictions on the functional forms. Of course this would mean controlling both function smoothness and model complexity.

Focus:

Non(semi)parametric techniques in high dimensions (well-posed)
Non(semi)parametric techniques in high dimensional ill-posed problems
Bandwidth choice in combination with complexity reduction
Statistical learning concepts (especially support vector machines)
Strategies bringing together nonparametric modelling and statistical learning
Numerical and computational concepts including needs for the future

Co-Chairs:

Michael G. Schimek
Institute for Medical Informatics, Statistics and Documentation
Medical University of Graz
Austria
Tel: +43316-385-4263
Fax: +43316-385-3590
E-mail: michael.schimek@meduni-graz.at

Ivana Horova
Department of Applied Mathematics
Masaryk University
Brno
Czech Republic
Tel: + 420 549 494 429
Fax: + 420 541 210 337
E-mail: horova@math.muni.cz
David W. Scott
Department of Statistics
Rice University
Houston Texas
USA
Tel: +1 713-348-- 6037
Fax: +1 713-348-5476
E-mail: scottdw@rice.edu