Conditional quantiles with varying Gaussians

DOI10.1007/s10444-011-9257-5zbMath1358.62044OpenAlexW2021044807MaRDI QIDQ1955538

Dao-Hong Xiang

Publication date: 14 June 2013

Published in: Advances in Computational Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10444-011-9257-5

zbMATH Keywords

learning theoryquantile regressionpinball lossvariance-expectation boundvarying Gaussian kernels

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Nonparametric estimation (62G05) General nonlinear regression (62J02) Learning and adaptive systems in artificial intelligence (68T05) Memory and learning in psychology (91E40)

Related Items (11)

Statistical consistency of coefficient-based conditional quantile regression ⋮ ERM scheme for quantile regression ⋮ Learning rates for the kernel regularized regression with a differentiable strongly convex loss ⋮ Convergence of online pairwise regression learning with quadratic loss ⋮ Quantitative convergence analysis of kernel based large-margin unified machines ⋮ Approximation analysis of learning algorithms for support vector regression and quantile regression ⋮ Quantile regression with \(\ell_1\)-regularization and Gaussian kernels ⋮ Convergence rate of SVM for kernel-based robust regression ⋮ Calibration of \(\epsilon\)-insensitive loss in support vector machines regression ⋮ Perturbation of convex risk minimization and its application in differential private learning algorithms ⋮ Moving quantile regression

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• ESTIMATING THE APPROXIMATION ERROR IN LEARNING THEORY
• On Complexity Issues of Online Learning Algorithms
• Theory of Reproducing Kernels

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