Cross-validating fit and predictive accuracy of nonlinear quantile regressions

DOI10.1080/02664763.2011.573542OpenAlexW2003763116MaRDI QIDQ5124973

Joachim Schnurbus, Harry Haupt, Kathrin Kagerer

Publication date: 30 September 2020

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2011.573542

zbMATH Keywords

model selectionsplinequantile regressionkernelcross validationmixed covariates

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Modelling credit card exposure at default using vine copula quantile regression

Uses Software

• R
• UCI-ml
• quantreg
• SemiPar
• FITPACK
• SparseM
• np

Cites Work

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• Quasi-maximum likelihood estimation for conditional quantiles
• Nonparametric models and their estimation
• Quantile splines with several covariates
• An algorithm for quantile smoothing splines
• Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
• Non-crossing quantile regression via doubly penalized kernel machine
• A Frisch-Newton algorithm for sparse quantile regression
• Convergence rate of b-spline estimators of nonparametric conditional quantile functions^∗
• Local Linear Quantile Regression
• Bivariate Quantile Smoothing Splines
• Monotone B-Spline Smoothing
• Smoothing Spline Score Estimation
• Quantile smoothing splines
• Semiparametric Regression
• Goodness of Fit and Related Inference Processes for Quantile Regression
• Penalized Triograms: Total Variation Regularization for Bivariate Smoothing
• Improved double kernel local linear quantile regression
• Semiparametric Estimation Methods for Panel Count Data Using MonotoneB-Splines
• Nonparametric Quantile Estimations for Dynamic Smooth Coefficient Models
• A practical guide to splines.

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