Optimal model selection in heteroscedastic regression using piecewise polynomial functions

DOI10.1214/13-EJS803zbMath1337.62083arXiv1104.1050MaRDI QIDQ1951154

Adrien Saumard

Publication date: 29 May 2013

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

Full work available at URL: https://arxiv.org/abs/1104.1050

zbMATH Keywords

nonparametric regressionrandom designheteroscedastic noiseslope heuristicsoptimal model selectionhold-out penalty

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) General nonlinear regression (62J02) Nonparametric statistical resampling methods (62G09)

Related Items (6)

Slope heuristics and V-Fold model selection in heteroscedastic regression using strongly localized bases ⋮ Discussion of ``On concentration for (regularized) empirical risk minimization by Sara van de Geer and Martin Wainwright ⋮ Estimator selection: a new method with applications to kernel density estimation ⋮ Concentration behavior of the penalized least squares estimator ⋮ Sharp oracle inequalities and slope heuristic for specification probabilities estimation in discrete random fields ⋮ Non-parametric Poisson regression from independent and weakly dependent observations by model selection

Uses Software

• CAPUSHE

Cites Work

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