conquer-package {conquer}R Documentation

Conquer: 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 \ell_1-penalization and iteratively reweighted \ell_1-penalization are used to fit sparse models. Commonly used penalities, such as the elastic-net, group lasso and sparse group lasso, are also incorporated to deal with more complex low-dimensional structures.


Xuming He <>, Xiaoou Pan <>, Kean Ming Tan <>, and Wen-Xin Zhou <>


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[Package conquer version 1.3.0 Index]