Finite sample performance of kernel-based regression methods for non-parametric additive models under common bandwidth selection criterion
From MaRDI portal
Publication:5297093
DOI10.1080/10485250701297933zbMath1116.62041OpenAlexW2047741214MaRDI QIDQ5297093
Publication date: 18 July 2007
Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)
Full work available at URL: https://mpra.ub.uni-muenchen.de/39295/1/MPRA_paper_39295.pdf
Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20) Monte Carlo methods (65C05)
Related Items
Kernel-based estimation of semiparametric regression in triangular systems ⋮ Nonparametric lag selection for nonlinear additive autoregressive models ⋮ Nonparametric additive model-assisted estimation for survey data ⋮ Oracally efficient spline smoothing of nonlinear additive autoregression models with simultaneous confidence band ⋮ Estimation of a partially linear additive model with generated covariates ⋮ More efficient local polynomial regression with random-effects panel data models
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Linear smoothers and additive models
- Additive regression and other nonparametric models
- Optimal rates of convergence for nonparametric estimators
- Fitting a bivariate additive model by local polynomial regression
- A general projection framework for constrained smoothing.
- Asymptotic properties of backfitting estimators
- Integration and backfitting methods in additive models -- finite sample properties and comparison
- Bandwidth selection for smooth backfitting in additive models
- An Effective Bandwidth Selector for Local Least Squares Regression
- Estimation of additive regression models with known links
- Design-adaptive Nonparametric Regression
- A Fully Automated Bandwidth Selection Method for Fitting Additive Models
- Versions of Kernel-Type Regression Estimators
- Nonparametric Identification of Nonlinear Time Series: Projections
- Miscellanea. Efficient estimation of additive nonparametric regression models
- A comparison of different nonparametric methods for inference on additive models
- Smooth Backfitting in Practice
- A kernel method of estimating structured nonparametric regression based on marginal integration