Simultaneous estimation of quantile regression functions using B-splines and total variation penalty

DOI10.1016/j.csda.2018.10.005OpenAlexW2896115672WikidataQ129045877 ScholiaQ129045877MaRDI QIDQ1727908

Ja-Yong Koo, Jae-Hwan Jhong

Publication date: 21 February 2019

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2018.10.005

zbMATH Keywords

total variationminimax ratecoordinate descent algorithmnon-crossingbinary methodweighted quantile

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Density estimation (62G07)

Uses Software

• COBS
• QICD

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection
• Penalized B-spline estimator for regression functions using total variation penalty
• Composite quantile regression and the oracle model selection theory
• Global nonparametric estimation of conditional quantile functions and their derivatives
• Estimating the dimension of a model
• On spline estimators and prediction intervals in nonparametric regression.
• Local asymptotics for regression splines and confidence regions
• Weak convergence and empirical processes. With applications to statistics
• COBS: qualitatively constrained smoothing via linear programming
• Pathwise coordinate optimization
• Coordinate descent algorithms for lasso penalized regression
• Noncrossing quantile regression curve estimation
• Comparing Automatic Smoothers (A Public Service Enterprise)
• Regression Quantiles
• Ideal spatial adaptation by wavelet shrinkage
• Quantile smoothing splines
• Adaptive estimation with soft thresholding penalties
• Penalized Triograms: Total Variation Regularization for Bivariate Smoothing
• Sparsity and Smoothness Via the Fused Lasso
• Quantile regression with monotonicity restrictions using P-splines and the L1-norm
• A fast and efficient implementation of qualitatively constrained quantile smoothing splines
• Regularization and Variable Selection Via the Elastic Net
• Some applications of concentration inequalities to statistics
• The elements of statistical learning. Data mining, inference, and prediction

This page was built for publication: Simultaneous estimation of quantile regression functions using B-splines and total variation penalty