|Title||Interquantile Shrinkage and Variable Selection in Quantile Regression.|
|Publication Type||Journal Article|
|Year of Publication||2014|
|Authors||Jiang, Liewen, Howard D. Bondell, and Huixia Judy Wang|
|Journal||Comput Stat Data Anal|
|Date Published||2014 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 Journal||Comput Stat Data Anal|
|Original Publication||Interquantile shrinkage and variable selection in quantile regression.|
|PubMed Central ID||PMC3956083|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States|
Interquantile Shrinkage and Variable Selection in Quantile Regression.