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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Communications in statistics. Theory and methods Ročník 53; číslo 1; s. 215 - 231
Hlavní autori: Zhang, Jie, Li, Yang, Zhao, Ni, Zheng, Zemin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Taylor & Francis 02.01.2024
Predmet:
Measurement errors
model selection
nearest positive semi-definite matrix projection
polynomial algorithm
regularization
ISSN:0361-0926, 1532-415X
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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 techniques of the nearest positive semi-definite matrix projection, the resulting regularization problem can be efficiently solved through a polynomial algorithm. Under some interpretable conditions, we prove that the proposed estimator can enjoy comprehensive statistical properties including the model selection consistency and the oracle inequalities. In particular, the nonoptimality of the logarithmic factor of dimensionality will be showed in the oracle inequalities. We demonstrate the effectiveness of the proposed method by simulation studies.
ISSN:0361-0926
1532-415X
DOI:10.1080/03610926.2022.2076125