Projected spline estimation of the nonparametric function in high-dimensional partially linear models for massive data

DOI10.1214/18-AOS1769zbMath1436.62145OpenAlexW2966857464MaRDI QIDQ2328064

Kaifeng Zhao, Heng Lian, Shao-Gao Lv

Publication date: 9 October 2019

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

Full work available at URL: https://projecteuclid.org/euclid.aos/1564797868

zbMATH Keywords

asymptotic normalityB-splineslocal asymptoticsprofiled estimation

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Numerical computation using splines (65D07) Ridge regression; shrinkage estimators (Lasso) (62J07) Asymptotic properties of nonparametric inference (62G20)

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A review of distributed statistical inference ⋮ Composite quasi-likelihood for single-index models with massive datasets ⋮ A two sample test based on U-statistic for functional data ⋮ Global debiased DC estimations for biased estimators via pro forma regression ⋮ Semiparametric function-on-function quantile regression model with dynamic single-index interactions ⋮ Communication-efficient distributed estimation of partially linear additive models for large-scale data ⋮ Unnamed Item ⋮ A split-and-conquer variable selection approach for high-dimensional general semiparametric models with massive data

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