Reducing variance in univariate smoothing

DOI10.1214/009053606000001398zbMath1117.62038arXiv0708.1815OpenAlexW2061797419MaRDI QIDQ995415

Ming-Yen Cheng, Jyh-Shyang Wu, Liang Peng

Publication date: 3 September 2007

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

Full work available at URL: https://arxiv.org/abs/0708.1815

zbMATH Keywords

bandwidthvariance reductioncoverage probabilitykernellocal linear regressionnonparametric smoothing

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05)

Related Items

Empirical likelihood-based inference for nonparametric recurrent diffusions ⋮ Reducing the mean squared error in kernel density estimation ⋮ Calibration with low bias ⋮ On the choice of difference sequence in a unified framework for variance estimation in nonparametric regression ⋮ Conditional variance estimation in heteroscedastic regression models ⋮ A doubly robustified estimating function for ARCH time series models ⋮ Variance reduction for kernel estimators in clustered/longitudinal data analysis ⋮ Averaging estimators for kernel regressions ⋮ Local quasi-likelihood with a parametric guide ⋮ Identification and wavelet estimation of weighted ATE under discontinuous and kink incentive assignment mechanisms

Uses Software

• KernSmooth

Cites Work

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