Quantile regression for longitudinal data
Journal of Multivariate Analysis - Special issue on semiparametric and nonparametric mixed models
A note on adaptive group lasso
Computational Statistics & Data Analysis
Regularized simultaneous model selection in multiple quantiles regression
Computational Statistics & Data Analysis
GACV for quantile smoothing splines
Computational Statistics & Data Analysis
Variable selection via combined penalization for high-dimensional data analysis
Computational Statistics & Data Analysis
Quadratic approximation on SCAD penalized estimation
Computational Statistics & Data Analysis
Split Bregman method for large scale fused Lasso
Computational Statistics & Data Analysis
Editorial for the special issue on quantile regression and semiparametric methods
Computational Statistics & Data Analysis
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Some regularization methods, including the group lasso and the adaptive group lasso, have been developed for the automatic selection of grouped variables (factors) in conditional mean regression. In many practical situations, such a problem arises naturally when a set of dummy variables is used to represent a categorical factor and/or when a set of basis functions of a continuous variable is included in the predictor set. Complementary to these earlier works, the simultaneous and automatic factor selection is examined in quantile regression. To incorporate the factor information into regularized model fitting, the adaptive sup-norm regularized quantile regression is proposed, which penalizes the empirical check loss function by the sum of factor-wise adaptive sup-norm penalties. It is shown that the proposed method possesses the oracle property. A simulation study demonstrates that the proposed method is a more appropriate tool for factor selection than the adaptive lasso regularized quantile regression.