A Parallel Algorithm for Large-Scale Nonconvex Penalized Quantile Regression

Penalized quantile regression (PQR) provides a useful tool for analyzing high-dimensional data with heterogeneity. However, its computation is challenging due to the nonsmoothness and (sometimes) the nonconvexity of the objective function. An iterative coordinate descent algorithm (QICD) was recentl...

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Vydáno v:	Journal of computational and graphical statistics Ročník 26; číslo 4; s. 935 - 939
Hlavní autoři:	Yu, Liqun, Lin, Nan, Wang, Lan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Alexandria Taylor & Francis 02.10.2017 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America Taylor & Francis Ltd
Témata:	ADMM Algorithms Computation Descent Dimensional analysis Iterative methods Nonconvex penalty Parallelization Quantile regression and single-loop algorithm Regression analysis Short Technical Notes Statistical analysis Studies
ISSN:	1061-8600, 1537-2715
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