An interpolation method for adapting to sparse design in multivariate nonparametric regression

DOI10.1016/S0378-3758(02)00184-2zbMath1020.62031OpenAlexW2017706032MaRDI QIDQ1400122

Wen-Shuenn Deng, Chih-Kang Chu

Publication date: 13 August 2003

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0378-3758(02)00184-2

zbMATH Keywords

Nadaraya-Watson estimatorlocal linear estimatorsimulationsinterpolation methodsparse designpseudodata

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Estimation in multivariate analysis (62H12)

Related Items (4)

On sufficient conditions for the consistency of local linear kernel estimators ⋮ Towards Insensitivity of Nadaraya--Watson Estimators to Design Correlation ⋮ Universal kernel-type estimation of random fields ⋮ A local multivariate Lagrange interpolation method for constructing shape functions

Uses Software

• KernSmooth

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Bandwidth choice for nonparametric regression
• Nonparametric regression analysis of longitudinal data
• Smoothing techniques. With implementation in S
• Exact mean integrated squared error
• Variable bandwidth and local linear regression smoothers
• On the role of the shrinkage parameter in local linear smoothing
• Choosing a kernel regression estimator. With comments and a rejoinder by the authors
• Multivariate locally weighted least squares regression
• Smoothing methods in statistics
• Local linear regression smoothers and their minimax efficiencies
• Smooth estimators of distribution and density functions
• Finite-Sample Variance of Local Polynomials: Analysis and Solutions
• An Effective Bandwidth Selector for Local Least Squares Regression
• The choice of weights in kernel regression estimation
• Double smoothing for kernelestimators in nonparametric regression
• A Unifying Approach to Nonparametric Regression Estimation
• Robust Locally Weighted Regression and Smoothing Scatterplots
• Design-adaptive Nonparametric Regression
• Empirical-Bias Bandwidths for Local Polynomial Nonparametric Regression and Density Estimation
• Interpolation Methods for Adapting to Sparse Design in Nonparametric Regression
• Enhancing convolution and interpolation methods for nonparametric regression
• Local Polynomial Kernel Regression for Generalized Linear Models and Quasi-Likelihood Functions
• Asymptotic properties of locally weighted regression ruey-ching hwang

This page was built for publication: An interpolation method for adapting to sparse design in multivariate nonparametric regression