Recovery of partly sparse and dense signals

Izuru Miyazaki

Journal of Multivariate Analysis, 2023, vol. 195, issue C

Abstract: In high-dimensional data analysis, we often encounter partly sparse and dense signals or parameters. Considering an lq-penalization with different qs for each sub-vector of the signals, we formularize an optimal solution for q=1 or 2 in a linear regression model to well represent such signals or parameters. We also provide an algorithm to derive it. Furthermore, we provide the consistency result of the variable selection in this optimal solution under a fixed design. Simulation study and real-data analysis illustrate its improved variable selection performance relative to the conventional methods.

Keywords: High-dimensional statistics; Prior knowledge; Regression; Regularization; Shrinkage estimator; Variable selection (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0047259X23000076
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:195:y:2023:i:c:s0047259x23000076

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.jmva.2023.105161

Access Statistics for this article

Journal of Multivariate Analysis is currently edited by de Leeuw, J.

More articles in Journal of Multivariate Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().