On the risk of convex-constrained least squares estimators under misspecification

DOI10.3150/18-BEJ1051zbMath1466.62333arXiv1706.04276OpenAlexW2963544243MaRDI QIDQ2419675

Billy Fang, Adityanand Guntuboyina

Publication date: 14 June 2019

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1706.04276

zbMATH Keywords

least squaresmisspecificationisotonic regressionconvex constraintstatistical dimension

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05) Parametric inference under constraints (62F30)

Related Items (3)

Nonparametric shape-restricted regression ⋮ Local continuity of log-concave projection, with applications to estimation under model misspecification ⋮ Distribution-free properties of isotonic regression

Cites Work

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• Sharp MSE bounds for proximal denoising
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• Risk bounds in isotonic regression
• Convergence of linear functionals of the Grenander estimator under misspecification
• Penalized isotonic regression
• Intrinsic volumes of polyhedral cones: a combinatorial perspective
• Nonparametric Estimation under Shape Constraints
• Living on the edge: phase transitions in convex programs with random data

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