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|a 10.1080/02664763.2020.1722079
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|a eng
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| 100 |
1 |
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|a Jiang, Yunlu
|e verfasserin
|4 aut
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| 245 |
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|a Outlier detection and robust variable selection via the penalized weighted LAD-LASSO method
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|c 2021
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|a Text
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|a ƒaComputermedien
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|a Date Revised 26.08.2024
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|a published: Electronic-eCollection
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|a Citation Status PubMed-not-MEDLINE
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|a © 2020 Informa UK Limited, trading as Taylor & Francis Group.
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|a This paper studies the outlier detection and robust variable selection problem in the linear regression model. The penalized weighted least absolute deviation (PWLAD) regression estimation method and the adaptive least absolute shrinkage and selection operator (LASSO) are combined to simultaneously achieve outlier detection, and robust variable selection. An iterative algorithm is proposed to solve the proposed optimization problem. Monte Carlo studies are evaluated the finite-sample performance of the proposed methods. The results indicate that the finite sample performance of the proposed methods performs better than that of the existing methods when there are leverage points or outliers in the response variable or explanatory variables. Finally, we apply the proposed methodology to analyze two real datasets
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|a Journal Article
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|a LASSO
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|a Outlier detection
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| 650 |
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|a penalized weighted least absolute deviation
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| 650 |
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|a robust regression
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| 650 |
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4 |
|a variable selection
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| 700 |
1 |
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|a Wang, Yan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Jiantao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xie, Baojian
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liao, Jibiao
|e verfasserin
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|a Liao, Wenhui
|e verfasserin
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|i Enthalten in
|t Journal of applied statistics
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|g 48(2021), 2 vom: 01., Seite 234-246
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