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research article

Designing Statistical Estimators That Balance Sample Size, Risk, and Computational Cost

Bruer, John J.
•
Tropp, Joel A.
•
Cevher, Volkan  orcid-logo
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2015
IEEE Journal of Selected Topics in Signal Processing

This paper proposes a tradeoff between computational time, sample complexity, and statistical accuracy that applies to statistical estimators based on convex optimization. When we have a large amount of data, we can exploit excess samples to decrease statistical risk, to decrease computational cost, or to trade off between the two. We propose to achieve this tradeoff by varying the amount of smoothing applied to the optimization problem. This work uses regularized linear regression as a case study to argue for the existence of this tradeoff both theoretically and experimentally. We also apply our method to describe a tradeoff in an image interpolation problem.

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Type
research article
DOI
10.1109/Jstsp.2015.2400412
Web of Science ID

WOS:000354480600004

Author(s)
Bruer, John J.
Tropp, Joel A.
Cevher, Volkan  orcid-logo
Becker, Stephen  
Date Issued

2015

Publisher

Ieee-Inst Electrical Electronics Engineers Inc

Published in
IEEE Journal of Selected Topics in Signal Processing
Volume

9

Issue

4

Start page

612

End page

624

Subjects

Convex optimization

•

image interpolation

•

regularized regression

•

resource tradeoffs

•

smoothing methods

•

statistical estimation

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LIONS  
Available on Infoscience
February 5, 2015
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/110843
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