A robust and efficient variable selection method for linear regression

Journal article


Yang, Zhuoran, Fu, Liya, Wang, You-Gan, Dong, Zhixiong and Jiang, Yunlu. (2021). A robust and efficient variable selection method for linear regression. Journal of Applied Statistics. 49(14), pp. 3677-3692. https://doi.org/10.1080/02664763.2021.1962259
AuthorsYang, Zhuoran, Fu, Liya, Wang, You-Gan, Dong, Zhixiong and Jiang, Yunlu
Abstract

Variable selection is fundamental to high dimensional statistical modeling, and many approaches have been proposed. However, existing variable selection methods do not perform well in presence of outliers in response variable or/and covariates. In order to ensure a high probability of correct selection and efficient parameter estimation, we investigate a robust variable selection method based on a modified Huber's function with an exponential squared loss tail. We also prove that the proposed method has oracle properties. Furthermore, we carry out simulation studies to evaluate the performance of the proposed method for both p<n and p>n. Our simulation results indicate that the proposed method is efficient and robust against outliers and heavy-tailed distributions. Finally, a real dataset from an air pollution mortality study is used to illustrate the proposed method.

KeywordsOracle properties; penalty function; robustness; variable selection
Year01 Jan 2021
JournalJournal of Applied Statistics
Journal citation49 (14), pp. 3677-3692
PublisherTaylor and Francis Ltd.
ISSN0266-4763
Digital Object Identifier (DOI)https://doi.org/10.1080/02664763.2021.1962259
PubMed ID36246863
PubMed Central IDPMC9559330
Web address (URL)https://www.tandfonline.com/doi/full/10.1080/02664763.2021.1962259
Open accessPublished as green open access
Research or scholarlyResearch
Page range3677-3692
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File Access Level
Controlled
Output statusPublished
Publication dates
Online06 Aug 2021
Publication process dates
Accepted26 Jul 2021
Deposited13 Jan 2023
Supplemental file
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ARC Funded ResearchThis output has been funded, wholly or partially, under the Australian Research Council Act 2001
Grant IDDP160104292
Additional information

© 2021 Informa UK Limited, trading as Taylor & Francis Group.

This research was supported by the National Natural Science Foundation of China (No. 11871390), Australian Research Council Discovery Project (DP160104292), the Fundamental Research Funds for the Central Universities (No. xjj2017180), the Natural Science Basic Research Plan in ShaanxiProvince of China (No. 2018JQ1006) and the Natural Science Foundation of Guangdong (Nos. 2018A030313171, 2019A1515011830).

Place of publicationUnited Kingdom
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