This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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560
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Type:
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Invited
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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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IMS
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Abstract - #306249 |
Title:
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A Unified Framework for High-Dimensional Analysis of M-estimators with Decomposable Regularizers
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Author(s):
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Pradeep Ravikumar*+ and Sahand Negahban and Martin Wainwright and Bin Yu
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Companies:
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The University of Texas at Austin and University of California, Berkeley and University of California, Berkeley and University of California, Berkeley
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Address:
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1 University Station C0500, Austin, TX, 78712, United States
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Keywords:
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M-estimators ;
High-dimensional ;
Regularization ;
Consistency ;
Convergence Rates ;
Sparsity
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Abstract:
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A line of recent work on high-dimensional statistical inference has studied models with various types of structure (e.g., sparse vectors; block-structured matrices; low-rank matrices, Markov assumptions). A general approach to estimation in such settings is to use a regularized M-estimator which combines a loss function (measuring goodness-of-fit of the models to the data) with some regularization function that encourages the assumed structure. Our goal is to provide a unified framework for establishing consistency and convergence rates for such regularized M-estimation procedures under high-dimensional scaling. We state one main theorem and show how it can be used to re-derive several existing results, and also to obtain several new results on consistency and convergence rates.
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Authors who are presenting talks have a * after their name.
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