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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Vydané v:	Journal of computational and graphical statistics Ročník 33; číslo 2; s. 625 - 637
Hlavní autori:	Man, Rebeka, Pan, Xiaoou, Tan, Kean Ming, Zhou, Wen-Xin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Alexandria Taylor & Francis 02.04.2024 Taylor & Francis Ltd
Predmet:	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
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