This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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400
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Computing
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Abstract - #306993 |
Title:
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Discovering Graphical Granger Causality Using the Truncating Lasso Penalty
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Author(s):
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Ali Shojaie*+ and George Michailidis
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Companies:
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University of Michigan and University of Michigan
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Address:
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269 West Hall 1085 South University Ave, Ann Arbor, MI, 48109,
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Keywords:
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Granger Causality ;
Graphical Models ;
Time Series ;
Penalized Likelihood ;
Truncating Lasso ;
Large p small n
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Abstract:
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Discovering causal relationships among random variables is the main goal in many statistical problems. However this may only be possible through randomized experiments. Time series observations provide a unique opportunity to determine how random variables affect each other over time and can be used to discover causal interactions. In this paper we propose a novel penalization method, truncating lasso, for estimation of causal relationships from time-course data based on the concept of Granger causality. This penalty can correctly determine the order of the underlying time series, and improves the performance of the lasso-type estimators. We provide an efficient algorithm for estimation of parameters and show prove variable selection consistency of the method in large p, small n settings. The performance of the proposed model is evaluated favorably in simulated, and real data examples.
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