Testing Against a Linear Regression Model Using Ideas from Shape-Restricted Estimation

DOI10.1111/rssb.12178zbMath1414.62148arXiv1311.6849OpenAlexW2963828666MaRDI QIDQ5381085

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Publication date: 7 June 2019

Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1311.6849

zbMATH Keywords

projectionasymptotic powerpartial linear modelmonotone functionadditive modeldouble coneclosed convex coneconvex regressionunbiased testnon-parametric likelihood ratio test

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Asymptotic properties of nonparametric inference (62G20) Linear regression; mixed models (62J05)

Related Items (7)

High-dimensional asymptotics of likelihood ratio tests in the Gaussian sequence model under convex constraints ⋮ Unnamed Item ⋮ Nonparametric shape-restricted regression ⋮ A framework for estimation and inference in generalized additive models with shape and order restrictions ⋮ The geometry of hypothesis testing over convex cones: generalized likelihood ratio tests and minimax radii ⋮ On Degrees of Freedom of Projection Estimators With Applications to Multivariate Nonparametric Regression ⋮ Sample selection models with monotone control functions

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