This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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589
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Type:
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Contributed
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Date/Time:
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Wednesday, August 4, 2010 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract - #308305 |
Title:
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Regularization, Sparsity, and Rank Restrictions in High-Dimensional Regression
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Author(s):
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Alan Julian Izenman*+
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Companies:
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Temple University
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Address:
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Department of Statistics, Speakman Hall, Philadelphia, PA, 19122-6083,
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Keywords:
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multivariate analysis ;
sparsity ;
reduced-rank regression ;
dimensionality reduction ;
regularization ;
graphical methods
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Abstract:
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As enormous data sets become the norm rather than the exception, statistics as a scientific discipline is changing to keep up with this development. Of particular interest are regression problems in which attention to high dimensionality has become an important part in determining how to proceed. In multiple regression, regularization and sparsity considerations have led to new methodologies for dealing with the high-dimensionality, low sample-size situation. In multivariate regression, rank restrictions have led to a reduced-rank regression model that incorporates many of the classical dimensionality-reduction methodologies as special cases. In this talk, we discuss problems of working with regression data when there are a large number of variables and a relatively small number of observations, and we explore some new graphical ideas in the multivariate case.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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