This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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41
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Type:
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Contributed
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Date/Time:
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Sunday, August 1, 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 - #307717 |
Title:
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LASSO-Patternsearch for Multivariate Bernoulli Observations with Applications
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Author(s):
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Bin Dai*+ and Stephen Wright and Xiwen Ma and Grace Wahba
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Companies:
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University of Wisconsin-Madison and University of Wisconsin-Madison and University of Wisconsin-Madison and University of Wisconsin-Madison
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Address:
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Department of Statistics, Madison, WI, 53706,
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Keywords:
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LASSO ;
Multivariate Bernoulli ;
Generalized linear model ;
GACV
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Abstract:
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The LASSO-Patternsearch algorithm is proposed to identify patterns efficiently that arise from the log linear expansion of the multivariate Bernoulli distribution. The method is designed for cases in which there is a very large number of candidate patterns but it is believed that relatively few are important. LASSO is used to reduce the number of candidate patterns greatly, using a novel computational algorithm that can handle a large number of unknowns simultaneously. The joint distribution conditioned on the predictor variables is estimated and the log odds ratio is used to measure the association among outcome variables. A data-adaptive tuning procedure based on GACV, modified to act as a model selector is proposed. Simulation studies and application to benchmark data set are conducted to check the performance of the proposed method.
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Authors who are presenting talks have a * after their name.
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