Convolution-Type Smoothed Quantile Regression

Estimation and inference for conditional linear quantile regression models using a convolution smoothed approach. In the low-dimensional setting, efficient gradient-based methods are employed for fitting both a single model and a regression process over a quantile range. Normal-based and (multiplier) bootstrap confidence intervals for all slope coefficients are constructed. In high dimensions, the conquer methods complemented with l_1-penalization and iteratively reweighted l_1-penalization are used to fit sparse models.


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1.2.1 by Xiaoou Pan, 3 months ago

Browse source code at

Authors: Xuming He [aut] , Xiaoou Pan [aut, cre] , Kean Ming Tan [aut] , Wen-Xin Zhou [aut]

Documentation:   PDF Manual  

GPL-3 license

Imports Rcpp, Matrix, matrixStats, stats, caret

Linking to Rcpp, RcppArmadillo

System requirements: C++11

Suggested by quantreg.

See at CRAN