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http://dx.doi.org/10.29220/CSAM.2019.26.4.359

A numerical study on group quantile regression models  

Kim, Doyoen (Department of Statistics, Korea University)
Jung, Yoonsuh (Department of Statistics, Korea University)
Publication Information
Communications for Statistical Applications and Methods / v.26, no.4, 2019 , pp. 359-370 More about this Journal
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.
Keywords
group penalty; penalized quantile regression; variable selection;
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