Selecting the amount of smoothing in nonparametric regression estimation for complex surveys

DOI10.1080/10485250500054642zbMath1065.62071OpenAlexW2081765768MaRDI QIDQ5460699

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Publication date: 18 July 2005

Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10485250500054642

zbMATH Keywords

local polynomial regressioncross-validationsimulationsmodel-assisted estimation

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20) Sampling theory, sample surveys (62D05)

Related Items (14)

Pseudo-empirical likelihood estimation using nonparametric regression ⋮ Nonparametric regression estimators in dual frame surveys ⋮ Model-Assisted Estimation Through Random Forests in Finite Population Sampling ⋮ A predictive estimator of finite population mean using nonparametric regression ⋮ Confidence bands for Horvitz-Thompson estimators using sampled noisy functional data ⋮ Analyzing establishment nonresponse using an interpretable regression tree model with linked administrative data ⋮ Nonparametric density estimation using partially rank-ordered set samples with application in estimating the distribution of wheat yield ⋮ Kernel regression estimators for nonparametric model calibration in survey sampling ⋮ On kernel nonparametric regression designed for complex survey data ⋮ Nonparametric estimation with mixed data types in survey sampling ⋮ Difference-based variance estimator for nonparametric regression in complex surveys ⋮ Nonparametric regression estimators in complex surveys ⋮ Mean estimation in the presence of change points ⋮ Local stationarity in small area estimation models

Uses Software

• KernSmooth

Cites Work

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• Optimal bandwidth selection in nonparametric regression function estimation
• Local polynomial regresssion estimators in survey sampling.
• Smoothing Parameter Selection in Nonparametric Regression Using an Improved Akaike Information Criterion
• On the Two Different Aspects of the Representative Method: The Method of Stratified Sampling and the Method of Purposive Selection
• A Model-Calibration Approach to Using Complete Auxiliary Information From Survey Data

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