The asymptotic normality of internal estimator for nonparametric regression
From MaRDI portal
Publication:824757
DOI10.1186/s13660-018-1832-6zbMath1498.62087OpenAlexW2889619936WikidataQ58754975 ScholiaQ58754975MaRDI QIDQ824757
Xiaoqin Li, Penghua Li, Li-Ping Chen
Publication date: 15 December 2021
Published in: Journal of Inequalities and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1186/s13660-018-1832-6
Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05)
Cites Work
- Uniform convergence of estimator for nonparametric regression with dependent data
- Strong consistency of the internal estimator of nonparametric regression with dependent data
- On internally corrected and symmetrized kernel estimators for nonparametric regression
- Nonparametric curve estimation from time series
- A distribution-free theory of nonparametric regression
- Nonlinear time series. Nonparametric and parametric methods
- Nonparametric regression estimation for dependent functional data: asymptotic normality
- Maximal moment inequality for partial sums of strong mixing sequences and application
- UNIFORM CONVERGENCE RATES FOR KERNEL ESTIMATION WITH DEPENDENT DATA
- Versions of Kernel-Type Regression Estimators
- A kernel method of estimating structured nonparametric regression based on marginal integration
- Inference and Prediction in Large Dimensions
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item