The problem of automatic bandwidth selection in nonparametric regression is considered when a local linear estimator is used to derive nonparametrically the unknown regression function. A plug-in method for choosing the smoothing parameter based on the use of the neural networks is presented. The method applies to dependent data generating processes with nonlinear autoregressive time series representation. The consistency of the method is shown in the paper, and a simulation study is carried out to assess the empirical performance of the procedure
This paper is concerned with approximating nonlinear time series by an artificial neural network bas...
We present a greedy method for simultaneously performing local band-width selection and variable sel...
We analyze the problem of estimating nonparametrically the volatility function of a financial time s...
The problem of automatic bandwidth selection in nonparametric regression is considered when a local ...
The selection of the smoothing parameter represents a crucial step in the local polynomial regressi...
A decisive question in nonparametric smoothing techniques is the choice of the bandwidth or smoothin...
We propose an adaptive smoothing method for nonparamet- ric regression. The central idea of the pro...
This paper is concerned with data-based selection of the bandwidth for a data sharpening estimator i...
When estimating a mean regression function and its derivatives, locally weighted least squares regre...
The selection of the smoothing parameter represents a crucial step in local polynomial regression, d...
Nonparametric estimation of abrupt changes in a regression function involves choosing smoothing (ban...
We propose an automated bandwidth selection procedure for the nonparametric estimation of conditiona...
We address the problem of prediction of nonlinear time series by kernel estimation of autoregression...
Härdle W, Marron JS. Optimal Bandwidth Selection in Nonparametric Regression Function Estimation. Th...
This paper is concerned with approximating nonlinear time series by an artificial neural network bas...
We present a greedy method for simultaneously performing local band-width selection and variable sel...
We analyze the problem of estimating nonparametrically the volatility function of a financial time s...
The problem of automatic bandwidth selection in nonparametric regression is considered when a local ...
The selection of the smoothing parameter represents a crucial step in the local polynomial regressi...
A decisive question in nonparametric smoothing techniques is the choice of the bandwidth or smoothin...
We propose an adaptive smoothing method for nonparamet- ric regression. The central idea of the pro...
This paper is concerned with data-based selection of the bandwidth for a data sharpening estimator i...
When estimating a mean regression function and its derivatives, locally weighted least squares regre...
The selection of the smoothing parameter represents a crucial step in local polynomial regression, d...
Nonparametric estimation of abrupt changes in a regression function involves choosing smoothing (ban...
We propose an automated bandwidth selection procedure for the nonparametric estimation of conditiona...
We address the problem of prediction of nonlinear time series by kernel estimation of autoregression...
Härdle W, Marron JS. Optimal Bandwidth Selection in Nonparametric Regression Function Estimation. Th...
This paper is concerned with approximating nonlinear time series by an artificial neural network bas...
We present a greedy method for simultaneously performing local band-width selection and variable sel...
We analyze the problem of estimating nonparametrically the volatility function of a financial time s...
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