A spectral series approach to high-dimensional nonparametric regression

DOI10.1214/16-EJS1112zbMath1332.62133arXiv1602.00355OpenAlexW2270035949MaRDI QIDQ5965330

Ann B. Lee, Rafael Izbicki

Publication date: 3 March 2016

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

Full work available at URL: https://arxiv.org/abs/1602.00355

zbMATH Keywords

high-dimensional inferencedata-driven basiseigenmapsmanifold learningMercer kernelorthogonal series regression

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08)

Related Items (8)

Multiscale regression on unknown manifolds ⋮ Comparing two populations using Bayesian Fourier series density estimation ⋮ Manifold learning with arbitrary norms ⋮ A spectral series approach to high-dimensional nonparametric regression ⋮ Cryo-EM reconstruction of continuous heterogeneity by Laplacian spectral volumes ⋮ ABC–CDE: Toward Approximate Bayesian Computation With Complex High-Dimensional Data and Limited Simulations ⋮ A Tale of Two Bases: Local-Nonlocal Regularization on Image Patches with Convolution Framelets ⋮ Spectral decomposition of atomic structures in heterogeneous cryo-EM

Uses Software

• IntDim

Cites Work

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