Normal approximation rate and bias reduction for data-driven kernel smoothing estimator in a semiparametric regression model

DOI10.1006/jmva.2000.1925zbMath0998.62038OpenAlexW2036954643MaRDI QIDQ1599064

Sheng-Yan Hong

Publication date: 31 July 2002

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1006/jmva.2000.1925

zbMATH Keywords

bias reductionnormal approximationsemiparametric regression modelbandwidth choiceBerry-Esseen ratedata-driven estimator

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20) Central limit and other weak theorems (60F05)

Related Items

Statistical inference for panel data semiparametric partially linear regression models with heteroscedastic errors, Block external bootstrap in partially linear models with nonstationary strong mixing error terms, Jackknife Estimation for Smooth Functions of the Parametric Component in Partially Linear Regression Models, Partially linear models and polynomial spline approximations for the analysis of unbalanced panel data, Block empirical likelihood for longitudinal partially linear regression models

Uses Software

• bootstrap

Cites Work

• Convergence rates for partially splined models
• How Far Are Automatically Chosen Regression Smoothing Parameters From Their Optimum?
• Root-N-Consistent Semiparametric Regression
• The Normal Approximation for Semiparametric Averaged Derivatives
• Some Comments on C P
• The bootstrap and Edgeworth expansion
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