Bandwidth selection for nonparametric regression with errors-in-variables
From MaRDI portal
Publication:6176094
DOI10.1080/07474938.2023.2191105OpenAlexW4366758995MaRDI QIDQ6176094
Hao Dong, Luke Taylor, Taisuke Otsu
Publication date: 25 July 2023
Published in: Econometric Reviews (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/07474938.2023.2191105
Cites Work
- Gaussian approximation of suprema of empirical processes
- Adaptive wavelet multivariate regression with errors in variables
- On the consistency of cross-validation in kernel nonparametric regression
- On the optimal rates of convergence for nonparametric deconvolution problems
- Deconvolution problems in nonparametric statistics
- Practical bandwidth selection in deconvolution kernel density estimation
- Optimal bandwidth selection in nonparametric regression function estimation
- Nonparametric regression with errors in variables
- Uniform confidence bands in deconvolution with unknown error distribution
- Bootstrap bandwidth selection in kernel density estimation from a contaminated sample
- Convolution without independence
- Identification of nonparametric monotonic regression models with continuous nonclassical measurement errors
- Uniform confidence bands for nonparametric errors-in-variables regression
- Regression discontinuity design with continuous measurement error in the running variable
- On deconvolution with repeated measurements
- A ridge-parameter approach to deconvolution
- Deconvolving kernel density estimators
- Nonparametric Regression Estimation in the Heteroscedastic Errors-in-Variables Problem
- Using SIMEX for Smoothing-Parameter Choice in Errors-in-Variables Problems
- How Far Are Automatically Chosen Regression Smoothing Parameters From Their Optimum?
- Optimal Rates of Convergence for Deconvolving a Density
- Nonparametric regression with infinite order flat-top kernels
- On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference
- Confidence Bands in Non-Parametric Errors-In-Variables Regression
- Instrumental Variable Treatment of Nonclassical Measurement Error Models
- Measurement Error in Nonlinear Models
- Random forests
This page was built for publication: Bandwidth selection for nonparametric regression with errors-in-variables
