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Activity Number: 96 - New Statistical Methods with Distributed and Parallel Algorithms
Type: Invited
Date/Time: Monday, July 31, 2017 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #322451
Title: High-Dimensional Non-Standard Regression
Author(s): Hui Zou*
Companies: University of Minnesota

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.

Authors who are presenting talks have a * after their name.

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