|Title||Interquantile Shrinkage in Regression Models.|
|Publication Type||Journal Article|
|Year of Publication||2013|
|Authors||Jiang, Liewen, Huixia Judy Wang, and Howard D. Bondell|
|Journal||J Comput Graph Stat|
Conventional analysis using quantile regression typically focuses on fitting the regression model at different quantiles separately. However, in situations where the quantile coefficients share some common feature, joint modeling of multiple quantiles to accommodate the commonality often leads to more efficient estimation. One example of common features is that a predictor may have a constant effect over one region of quantile levels but varying effects in other regions. To automatically perform estimation and detection of the interquantile commonality, we develop two penalization methods. When the quantile slope coefficients indeed do not change across quantile levels, the proposed methods will shrink the slopes towards constant and thus improve the estimation efficiency. We establish the oracle properties of the two proposed penalization methods. Through numerical investigations, we demonstrate that the proposed methods lead to estimations with competitive or higher efficiency than the standard quantile regression estimation in finite samples. Supplemental materials for the article are available online.
|Alternate Journal||J Comput Graph Stat|
|Original Publication||Interquantile shrinkage in regression models.|
|PubMed Central ID||PMC3867140|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States|
Interquantile Shrinkage in Regression Models.