A Unified Algorithm for Penalized Convolution Smoothed Quantile Regression

Penalized quantile regression (QR) is widely used for studying the relationship between a response variable and a set of predictors under data heterogeneity in high-dimensional settings. Compared to penalized least squares, scalable algorithms for fitting penalized QR are lacking due to the non-diff...

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Veröffentlicht in:	Journal of computational and graphical statistics Jg. 33; H. 2; S. 625 - 637
Hauptverfasser:	Man, Rebeka, Pan, Xiaoou, Tan, Kean Ming, Zhou, Wen-Xin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Alexandria Taylor & Francis 02.04.2024 Taylor & Francis Ltd
Schlagworte:	Algorithms Convolution Convolution smoothing Heterogeneity Lasso Majorize-minimization algorithm Penalized optimization Quantile estimation regression Quantiles Regression Smoothness Statistical analysis Statistical methods
ISSN:	1061-8600, 1537-2715
Online-Zugang:	Volltext
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