L 0-regularization for high-dimensional regression with corrupted data

Corrupted data appears widely in many contemporary applications including voting behavior, high-throughput sequencing and sensor networks. In this article, we consider the sparse modeling via L 0 -regularization under the framework of high-dimensional measurement error models. By utilizing the techn...

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Veröffentlicht in:	Communications in statistics. Theory and methods Jg. 53; H. 1; S. 215 - 231
Hauptverfasser:	Zhang, Jie, Li, Yang, Zhao, Ni, Zheng, Zemin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Taylor & Francis 02.01.2024
Schlagworte:	Measurement errors model selection nearest positive semi-definite matrix projection polynomial algorithm regularization
ISSN:	0361-0926, 1532-415X
Online-Zugang:	Volltext
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