A numerical study on group quantile regression models

Doyoen Kim, Yoonsuh Jung

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)359-370
Number of pages12
JournalCommunications for Statistical Applications and Methods
Issue number4
Publication statusPublished - 2019

Bibliographical note

Funding Information:
Yoonsuh Jung’s work was partially supported by National Research Foundation of Korea Grant NRF-2017R1C1B5017431.

Publisher Copyright:
© 2019 The Korean Statistical Society, and Korean International Statistical Society.


  • Group penalty
  • Penalized quantile regression
  • Variable selection

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation
  • Finance
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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