Selection of variables and dimension reduction in high-dimensional non-parametric regression

DOI10.1214/08-EJS327zbMath1320.62085arXiv0811.1115MaRDI QIDQ1951796

Guillaume Lecué, Karine Bertin

Publication date: 24 May 2013

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

Full work available at URL: https://arxiv.org/abs/0811.1115

zbMATH Keywords

dimension reductionLassohigh dimension

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08)

Related Items (16)

Statistical inference in sparse high-dimensional additive models ⋮ Improvement on LASSO-type estimator in nonparametric regression ⋮ Variable selection in heteroscedastic single-index quantile regression ⋮ GRID: a variable selection and structure discovery method for high dimensional nonparametric regression ⋮ Bias-corrected inference for multivariate nonparametric regression: model selection and oracle property ⋮ High-dimensional local linear regression under sparsity and convex losses ⋮ Learning sparse gradients for variable selection and dimension reduction ⋮ Statistical inference in compound functional models ⋮ Kernel Meets Sieve: Post-Regularization Confidence Bands for Sparse Additive Model ⋮ Variable selection of high-dimensional non-parametric nonlinear systems by derivative averaging to avoid the curse of dimensionality ⋮ Minimal conditions for consistent variable selection in high dimension ⋮ Sharp variable selection of a sparse submatrix in a high-dimensional noisy matrix ⋮ Variable selection with Hamming loss ⋮ Tight conditions for consistency of variable selection in the context of high dimensionality ⋮ Variable selection consistency of Gaussian process regression ⋮ Adaptive estimation of multivariate piecewise polynomials and bounded variation functions by optimal decision trees

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