Activity Number:
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314
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2016 : 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 #320170
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View Presentation
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Title:
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Nonparametric Regression with Adaptive Smoothness via a Convex Hierarchical Penalty
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Author(s):
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Asad Haris* and Ali Shojaie and Noah Simon
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Companies:
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University of Washington and University of Washington and University of Washington
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Keywords:
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Nonparametric regression ;
Convex optimization ;
Big data ;
Penalized regression ;
Additive models ;
Convergence rates
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Abstract:
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We consider the task of fitting a nonparametric regression model using a growing set of basis functions. Like projection estimators, our proposal fits a truncated basis expansion. The novelty of our method lies in the data-driven mechanism for selection of the truncation level which is achieved by nested group penalties. We establish convergence rates comparable to smoothing splines via entropy methods and, a closed form solution for the resulting optimization problem. We explore its performance in a simulation study and on real data.
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Authors who are presenting talks have a * after their name.