Title: Model Selection, Computational Methods,
and Optimization Heuristics
Description:
In recent years, the statistical literature has
placed more and more emphasis on model selection criteria. The problem is to
choose the best approximating model among a class of competing models by a
suitable model selection criterion given a finite data set. That model which
optimizes the criterion is chosen to be the best model. Therefore, the goal of
model selection is to optimize the quality of inference, and it advocates good
modeling. The emphasis on model selection can be traced back to the pioneering
work of Akaike and his classic: Akaike's (1973) information criterion (AIC).
Since then, a number of other model selection criteria such as Bayesian Model
Selection, Minimum Description Length, Information Complexity, and Minimum
Message Length criteria, and others have been proposed, each with its own
rationale and point of view. The developments and the introduction of model
selection criteria and techniques and recent advancements in personal computers
have marked the beginning of an era of systematic approach to model evaluation
and selection. These techniques have become popular in many cross-disciplinary
fields of science such as economics, biology, bio-informatics, data mining,
ecology, engineering, medicine, psychology, the social and behavioral sciences,
to mention a few.
The statistical techniques for model selection increasingly rely on demanding
and sophisticated numerical and computational methods. Estimation and modeling
as well as model selection raise many optimization problems which turn out to be
intractable by standard numerical methods in high dimensional data. One way to
deal with such a situation consists in simplifying models and/or applying new
and fast algorithms and optimization heuristics such as local search methods (Simulated
Annealing, Threshold Accepting, Tabu Search), Neural Networks, Genetic
Algorithms, and hybrid methods and many others.
This track on "Model Selection, Computational Methods, and Optimization
Heuristics" invites proposals for sessions covering on one hand statistical and
computational aspects of model selection as well as real applications in
cross-disciplinary fields; and on the other hand contributions which address
optimization problems using model selection as the fitness function by making
use of modern algorithms and heuristics.
If you are interested in organizing a session,
please contact one of the track co-chairs: Prof. Bozdogan, or Gilli, or Winker.
Details will be communicated with each session organizers.
Co-Chairs:
Hamparsum Bozdogan,
McKenzie Professor in Business
Department of Statistics, 336 SMC
The University of Tennessee
Knoxville, TN 37996-0532 USA
Tel: + 865 974-2556
Fax: + 865 974-2490
WWW: http://web.utk.edu/~bozdogan
E-mail: bozdogan@utk.edu
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Manfred Gilli
Department of Econometrics
University of Geneva
40, Bd du Pont d'Arve
1211 Geneve 4
Switzerland
Tel: +41 22 37 98222
Fax: +41 22 37 98299
E-mail Manfred.Gilli@metri.unige.ch |
Peter Winker
Faculty of Economics, Law and Social Sciences
University of Erfurt
P.O. Box 900221
D-99105 Erfurt, Germany
Tel: +49-361-737-4591
Fax: +49-361-737-4599
E-mail: Peter.Winker@uni-erfurt.de
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