Activity Number:
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96
- New Statistical Methods with Distributed and Parallel Algorithms
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Type:
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Invited
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Date/Time:
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Monday, July 31, 2017 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #322451
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Title:
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High-Dimensional Non-Standard Regression
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Author(s):
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Hui Zou*
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Companies:
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University of Minnesota
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Keywords:
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Abstract:
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Least squares regression is the standard method for regression analysis. Its high-dimensional generalizations have been extensively studied and widely used in practice. In this talk I will present several non-standard regression techniques for high-dimensional regression analysis. These methods have unique advantages over the standard least square regression. The corresponding optimization problem can be efficiently solved via modern optimization techniques. As a result, these non-standard regression methods can be used as effective competitors to the least squares regression.
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Authors who are presenting talks have a * after their name.
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