Plug-in bandwidth choice in partial linear models with autoregressive errors
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Publication:5956233
DOI10.1016/S0378-3758(01)00094-5zbMath1007.62030MaRDI QIDQ5956233
Germán Aneiros-Pérez, Alejandro Quintela-del-Río
Publication date: 18 March 2003
Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)
Nonparametric regression and quantile regression (62G08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05)
Related Items
On bandwidth selection in partial linear regression models under dependence ⋮ Partly linear models on Riemannian manifolds ⋮ Asymptotic Normality of Estimators in Heteroscedastic Semi-Parametric Model with Strong Mixing Errors ⋮ On inference for a semiparametric partially linear regression model with serially correlated errors ⋮ L1 penalty and shrinkage estimation in partially linear models with random coefficient autoregressive errors ⋮ Berry-Esseen type bounds in heteroscedastic semi-parametric model ⋮ Asymptotic normality in partial linear models based on dependent errors ⋮ Testing in partial linear regression models with dependent errors ⋮ Robust bandwidth selection in semiparametric partly linear regression models: Monte Carlo study and influential analysis ⋮ Statistical inferences in a partially linear model with autoregressive errors
Cites Work
- Data-driven bandwidth choice for density estimation based on dependent data
- Convergence rates for partially splined models
- Convergence rates for parametric components in a partly linear model
- Mixing properties of ARMA processes
- Central limit theorems under weak dependence
- Fixed design regression for time series: Asymptotic normality
- Time series: theory and methods.
- Consistent nonparametric regression. Discussion
- Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
- Comparing nonparametric versus parametric regression fits
- Mixing: Properties and examples
- Comparison of bandwidth selectors in nonparametric regression under dependence
- Asymptotic properties in partial linear models under dependence
- Efficient estimation in a semiparametric additive regression model with autoregressive errors
- Weighted least squares estimates in partly linear regression models
- Smoothing methods in statistics
- Transformations for Estimation of Linear Models with Nested-Error Structure
- Boundary modification for kernel regression
- Root-N-Consistent Semiparametric Regression
- KERNEL REGRESSION SMOOTHING OF TIME SERIES
- Bandwidth Choice for Average Derivative Estimation
- An Adaptive Estimator of the Autocorrelation Coefficient in Regression Models with Autoregressive Errors
- Moment bounds for non-stationary dependent sequences
- Asymptotic theory for partly linear models
- MODIFIED CROSS-VALIDATION IN SEMIPARAMETRIC REGRESSION MODELS WITH DEPENDENT ERRORS
- A plug-in technique in nonparametric regression with dependence
- Second Order Approximation in the Partially Linear Regression Model
- Predicting Using Box-Jenkins, Nonparametric, and Bootstrap Techniques
- Remarks on Non-Parametric Estimates for Density Functions and Regression Curves
- THE AUTOCORRELATION FUNCTION AND THE SPECTRAL DENSITY FUNCTION
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