Tuning parameter selection for nonparametric derivative estimation in random design

DOI10.1080/02331888.2023.2278042OpenAlexW4388491635MaRDI QIDQ6192202

Sisheng Liu, Richard Charnigo

Publication date: 12 February 2024

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331888.2023.2278042

zbMATH Keywords

heteroskedasticitytuning parameter selectionrandom covariatenonparametric derivative estimationempirical derivative

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

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• Conditional variance estimation in heteroscedastic regression models
• Bandwidth choice for differentiation
• Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
• Applied functional data analysis. Methods and case studies
• Estimator selection: a new method with applications to kernel density estimation
• On a Nadaraya-Watson estimator with two bandwidths
• Gradient-based smoothing parameter selection for nonparametric regression estimation
• Optimal smoothing in nonparametric conditional quantile derivative function estimation
• Self-consistent estimation of mean response functions and their derivatives
• Bandwidth choice and confidence intervals for derivatives of noisy data
• Smoothing Parameter Selection in Nonparametric Regression Using an Improved Akaike Information Criterion
• Efficient estimation of conditional variance functions in stochastic regression
• Local Regression and Likelihood
• On Difference-Based Variance Estimation in Nonparametric Regression When the Covariate is High Dimensional
• Some Comments on C P

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