2021
DOI: 10.1007/s00362-021-01229-0
|View full text |Cite
|
Sign up to set email alerts
|

Penalized and constrained LAD estimation in fixed and high dimension

Abstract: Recently, many literatures have proved that prior information and structure in many application fields can be formulated as constraints on regression coefficients. Following these work, we propose a L 1 penalized LAD estimation with some linear constraints in this paper. Different from constrained lasso, our estimation performs well when heavy-tailed errors or outliers are found in the response. In theory, we show that the proposed estimation enjoys the Oracle property with adjusted normal variance when the di… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Year Published

2023
2023
2025
2025

Publication Types

Select...
4
1

Relationship

0
5

Authors

Journals

citations
Cited by 5 publications
references
References 72 publications
0
0
0
Order By: Relevance