Title: Robust and Nonparametric Methods
Description:
Robust and nonparametric methods provide an
effective way of dealing with the many fundamental problems associated with
least squares techniques. Included are valid statistical inferences for
situations where models based on normality break down. Nonparametric and robust
methods can yield substantial gains in power, highly accurate confidence
intervals in situations where classic least squares methods perform poorly, and
they provide alternative perspectives that deepen and enhance our understanding
of data. Our goal is to bring together the most recent advances in the field.
Topics can include, but are not limited to regression, learning models,
clustering, ANOVA, outlier detection, techniques for general models, diagnostic
methods, and adjustment for covariates.
Co-Chairs:
Rand Wilcox
Dept of Psychology
University of Southern California
Los Angeles, CA 90089-1061, USA E-mail: rwilcox@usc.edu
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Simon Sheather
Australian Graduate School of Management
University of New Souths Wales
Sydney, New South Wales
Australia
E-mail: simonsh@agsm.edu.au
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Edgar Brunner
Abt. Med. Statistik
Universitat Gottingen
Humboldt Allee 32
D-37073 Gottingen
Germany
E-mail: brunner@ams.med.uni-goettingen.de
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