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A numerical study on group quantile regression models

Authors
Kim, DoyoenJung, Yoonsuh
Issue Date
7월-2019
Publisher
KOREAN STATISTICAL SOC
Keywords
group penalty; penalized quantile regression; variable selection
Citation
COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS, v.26, no.4, pp.359 - 370
Indexed
SCOPUS
KCI
Journal Title
COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS
Volume
26
Number
4
Start Page
359
End Page
370
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/64655
DOI
10.29220/CSAM.2019.26.4.359
ISSN
2287-7843
Abstract
Grouping structures in covariates are often ignored in regression models. Recent statistical developments considering grouping structure shows clear advantages; however, reflecting the grouping structure on the quantile regression model has been relatively rare in the literature. Treating the grouping structure is usually conducted by employing a group penalty. In this work, we explore the idea of group penalty to the quantile regression models. The grouping structure is assumed to be known, which is commonly true for some cases. For example, group of dummy variables transformed from one categorical variable can be regarded as one group of covariates. We examine the group quantile regression models via two real data analyses and simulation studies that reveal the beneficial performance of group quantile regression models to the non-group version methods if there exists grouping structures among variables.
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