Interquantile Shrinkage and Variable Selection in Quantile Regression.

TitleInterquantile Shrinkage and Variable Selection in Quantile Regression.
Publication TypeJournal Article
Year of Publication2014
AuthorsJiang, Liewen, Howard D. Bondell, and Huixia Judy Wang
JournalComput Stat Data Anal
Date Published2014 Jan 01

Examination of multiple conditional quantile functions provides a comprehensive view of the relationship between the response and covariates. In situations where quantile slope coefficients share some common features, estimation efficiency and model interpretability can be improved by utilizing such commonality across quantiles. Furthermore, elimination of irrelevant predictors will also aid in estimation and interpretation. These motivations lead to the development of two penalization methods, which can identify the interquantile commonality and nonzero quantile coefficients simultaneously. The developed methods are based on a fused penalty that encourages sparsity of both quantile coefficients and interquantile slope differences. The oracle properties of the proposed penalization methods are established. Through numerical investigations, it is demonstrated that the proposed methods lead to simpler model structure and higher estimation efficiency than the traditional quantile regression estimation.

Alternate JournalComput Stat Data Anal
Original PublicationInterquantile shrinkage and variable selection in quantile regression.
PubMed ID24653545
PubMed Central IDPMC3956083
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States