GRID: a variable selection and structure discovery method for high dimensional nonparametric regression

DOI10.1214/19-AOS1846zbMath1454.62125MaRDI QIDQ2196249

Maria Lucia Parrella, Francesco Giordano, Soumendra Nath Lahiri

Publication date: 28 August 2020

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.aos/1594972841

zbMATH Keywords

empirical likelihoodvariable selection consistencymarginal testing

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Nonparametric hypothesis testing (62G10) Asymptotic properties of nonparametric inference (62G20) Hypothesis testing in multivariate analysis (62H15)

Related Items (4)

A nonparametric procedure for linear and nonlinear variable screening ⋮ High-dimensional local polynomial regression with variable selection and dimension reduction ⋮ Linear and nonlinear signal detection and estimation in high-dimensional nonparametric regression under weak sparsity ⋮ High-dimensional local linear regression under sparsity and convex losses

Uses Software

• glasso

Cites Work

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