Information-Based Optimal Subdata Selection for Big Data Linear Regression-Reference-Cited by-同舟云学术

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Information-Based Optimal Subdata Selection for Big Data Linear Regression

Published:2018-06-28 Issue:525 Volume:114 Page:393-405
ISSN:0162-1459
Container-title:Journal of the American Statistical Association
language:en
Short-container-title:Journal of the American Statistical Association

Author:

Wang HaiYing¹,Yang Min²,Stufken John³

Affiliation:

1. Department of Statistics, University of Connecticut, Storrs, Mansfield, CT

2. Department of Mathematics, Statistics, and Computer Science, University of Illinois at Chicago, Chicago, IL

3. School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ

Funder

Division of Mathematical Sciences

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2017.1408468

Reference24 articles.

1. The Dantzig selector: Statistical estimation when p is much larger than n

2. Faster least squares approximation

3. Sampling algorithms for l2 regression and applications

4. Sure independence screening for ultrahigh dimensional feature space

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