Large Sample Properties of Partitioning-Based Series Estimators

DOI10.48550/arXiv.1804.04916zbMath1457.62108arXiv1804.04916MaRDI QIDQ143957

Yingjie Feng, Max H. Farrell, Matias D. Cattaneo, Matias D. Cattaneo, Max H. Farrell, Yingjie Feng

Publication date: 13 April 2018

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

Full work available at URL: https://arxiv.org/abs/1804.04916

zbMATH Keywords

nonparametric regressionstrong approximationsieve methodstuning parameter selectionrobust bias correction

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Numerical computation using splines (65D07) Asymptotic properties of nonparametric inference (62G20) Nontrigonometric harmonic analysis involving wavelets and other special systems (42C40) Monte Carlo methods (65C05)

Related Items (7)

Coverage error optimal confidence intervals for local polynomial regression ⋮ Debiased machine learning of set-identified linear models ⋮ A nonparametric predictive regression model using partitioning estimators based on Taylor expansions ⋮ Local regression distribution estimators ⋮ lspartition ⋮ INFERENCE IN NONPARAMETRIC SERIES ESTIMATION WITH SPECIFICATION SEARCHES FOR THE NUMBER OF SERIES TERMS ⋮ A NONPARAMETRIC TEST OF SIGNIFICANT VARIABLES IN GRADIENTS

Uses Software

• SemiPar
• lspartition
• binsreg

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