Statistics and Its Interface

Volume 11 (2018)

Number 3

Discussion on “Double sparsity kernel learning with automatic variable selection and data extraction”

Pages: 423 – 424

DOI: https://dx.doi.org/10.4310/SII.2018.v11.n3.a3

Authors

Meimei Liu (Department of Statistics, Purdue University, West Lafayette, Indiana, U.S.A.)

Guang Cheng (Department of Statistics, Purdue University, West Lafayette, Indiana, U.S.A.)

Abstract

DOSK proposed in [2] aims to perform both variable selection and data extraction at the same time under the “finite sparsity” assumption. In this short note, we propose two alternative approaches based on random projection and importance sampling without such an assumption. Furthermore, we compare these two methods with DOSK empirically in terms of statistical accuracy and computing efficiency.

Keywords

data extraction, importance sampling, kernel regression, reproducing kernel Hilbert space, random projection, variable selection

Full Text (PDF format)

Received 12 March 2018

Published 17 September 2018