Nonparametric multiple expectile regression via ER-Boost

DOI10.1080/00949655.2013.876024zbMath1457.62124OpenAlexW1975445189MaRDI QIDQ5220800

Yi Yang, Hui Zou

Publication date: 27 March 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2013.876024

zbMATH Keywords

asymmetric least squaresregression treegradient boostingfunctional gradient descentexpectile regression

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Linear regression; mixed models (62J05)

Related Items

Two-step sparse boosting for high-dimensional longitudinal data with varying coefficients, A new GEE method to account for heteroscedasticity using asymmetric least-square regressions, An SVM-like approach for expectile regression, Asymmetric influence measure for high dimensional regression, Iteratively reweighted least square for kernel expectile regression with random features, Dimension reduction techniques for conditional expectiles, Bayesian regularisation in geoadditive expectile regression, Learning rates for kernel-based expectile regression, erboost, KLERC: kernel Lagrangian expectile regression calculator, Local polynomial expectile regression, Efficient estimation in expectile regression using envelope models, Dynamic large financial networks \textit{via} conditional expected shortfalls, An elastic-net penalized expectile regression with applications

Uses Software

• R
• gbm
• AdaBoost.MH
• erboost

Cites Work

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