A critical review of univariate non-parametric estimation of first derivatives

DOI10.1080/00949655.2022.2070749OpenAlexW4293062234MaRDI QIDQ5055251

David Bernstein

Publication date: 13 December 2022

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

Full work available at URL: https://doi.org/10.1080/00949655.2022.2070749

zbMATH Keywords

kernel smoothingderivative estimationleast-squares cross-validationtuning parameternon-parametric regression

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• SemiPar

Cites Work

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• Bandwidth choice for differentiation
• Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
• Gradient-based bandwidth selection for estimating average derivatives
• A generalized correlated \(C_p\) criterion for derivative estimation with dependent errors
• Nonparametric estimation of the determinants of inefficiency in the presence of firm heterogeneity
• Computation and application of robust data-driven bandwidth selection for gradient function estimation
• Testing heteroscedasticity in nonparametric regression based on trend analysis
• Gradient-based smoothing parameter selection for nonparametric regression estimation
• Optimal smoothing in nonparametric conditional quantile derivative function estimation
• Bandwidth choice and confidence intervals for derivatives of noisy data
• Smoothing Parameter Selection in Nonparametric Regression Using an Improved Akaike Information Criterion
• Semiparametric Regression
• Applied Nonparametric Econometrics
• Estimating the Interest Rate Term Structure of Corporate Debt With a Semiparametric Penalized Spline Model
• Local weighted composite quantile estimation and smoothing parameter selection for nonparametric derivative function

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