Bootstrap selection of bandwidth and confidence bands for nonparametric regression

DOI10.1080/00949659008811292zbMath0775.62092OpenAlexW2031362163MaRDI QIDQ3135417

Julian J. Faraway

Publication date: 7 October 1993

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949659008811292

Mathematics Subject Classification ID

Density estimation (62G07) Nonparametric tolerance and confidence regions (62G15)

Related Items (9)

Automatic bandwidth choice and confidence intervals in nonparametric regression ⋮ A Complete Framework for Model-Free Difference-in-Differences Estimation ⋮ Model robust regression: combining parametric, nonparametric, and semiparametric methods ⋮ Smoothing for small samples with model misspecification: Nonparametric and semiparametric concerns ⋮ Iterated Bootstrap‐t Confidence Intervals for Density Functions ⋮ Simultaneous bootstrap confidence bands in nonparametric regression ⋮ Adaptive confidence interval for pointwise curve estimation. ⋮ Random rates in anisotropic regression. (With discussion) ⋮ An overview of model-robust regression

Cites Work

• Using the bootstrap to estimate mean squared error and select smoothing parameter in nonparametric problems
• On confidence bands in nonparametric density estimation and regression
• Bootstrap Prediction Intervals for Regression
• Smooth nonparametric quantile estimation under censoring: simulations and bootstrap methods
• Bootstrapping in Nonparametric Regression: Local Adaptive Smoothing and Confidence Bands
• How Far Are Automatically Chosen Regression Smoothing Parameters From Their Optimum?

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