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Activity Number:
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60
- Nonparametrics in High-Dimensional Data
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Type:
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Contributed
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Date/Time:
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Monday, August 3, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #312348
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Title:
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Low Rank Approximation for Smoothing Spline via Eigensystem Truncation
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Author(s):
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Danqing Xu* and Yuedong Wang
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Companies:
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Columbia University and UC Santa Barbara
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Keywords:
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Low Rank Approximation;
Eigensystem;
Smoothing Spline;
Reproducing Kernel Hilbert Space;
Approximation Error
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Abstract:
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Smoothing splines provide powerful and flexible means for nonparamatric estimation and inference. With a cubic time complexity, fitting smoothing spline models to large data is computationally prohibitive. In this paper we use the theoretical optimal eigenspace to derive a low rank approximation of the smoothing spline estimates. We develop a method to approximate the eigensystem when it is unknown and derive error bounds for the approximate estimates. The proposed methods are easy to implement with existing software. Extensive simulations show that the new methods are accurate, fast, and compares favorably against existing methods.
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Authors who are presenting talks have a * after their name.
