Add to ORKGAbstract: We propose a method for incorporating variable selection into local polynomial regression. This can improve the accuracy of the regression by extending the bandwidth in directions corresponding to those variables judged to be are unimportant. It also increases our understanding of the dataset by highlighting areas where these variables are redundant. The approach has the potential to effect complete variable removal as well as perform partial removal when a variable redundancy applies only to particular regions of the data. We define a nonparametric oracle property and show that this is more than satisfied by our approach under asymptotic analysis. The usefulness of the method is demonstrated through simulated and real data numeri...
Local polynomial regression is commonly used for estimating regression functions. In practice, howev...
The local polynomial estimator is particularly affected by the curse of dimensionality, which reduce...
Nonparametric regression techniques provide an effective way of identifying and examining structure ...
Nonparametric estimators are particularly affected by the curse of dimensionality. An interesting me...
Data-analytic approaches to regression problems, arising from many scientific disciplines are descri...
This thesis examines local polynomial regression. Local polynomial regression is one of non-parametr...
A decisive question in nonparametric smoothing techniques is the choice of the bandwidth or smoothin...
When estimating a regression function or its derivatives, local polynomials are an attractive choice...
Applying nonparametric variable selection criteria in nonlinear regression models generally requires...
The selection of the smoothing parameter represents a crucial step in local polynomial regression, d...
Local polynomial fitting has been known as a powerful nonparametric regression method when dealing w...
This paper proposes a classical weighted least squares type of local polynomial smoothing for the an...
When estimating a mean regression function and its derivatives, locally weighted least squares regre...
We investigate the extension of binning methodology to fast computation of several auxiliary quantit...
Local polynomial modelling can be seen as a local fit of the data against a polynomial basis. In thi...
Local polynomial regression is commonly used for estimating regression functions. In practice, howev...
The local polynomial estimator is particularly affected by the curse of dimensionality, which reduce...
Nonparametric regression techniques provide an effective way of identifying and examining structure ...
Nonparametric estimators are particularly affected by the curse of dimensionality. An interesting me...
Data-analytic approaches to regression problems, arising from many scientific disciplines are descri...
This thesis examines local polynomial regression. Local polynomial regression is one of non-parametr...
A decisive question in nonparametric smoothing techniques is the choice of the bandwidth or smoothin...
When estimating a regression function or its derivatives, local polynomials are an attractive choice...
Applying nonparametric variable selection criteria in nonlinear regression models generally requires...
The selection of the smoothing parameter represents a crucial step in local polynomial regression, d...
Local polynomial fitting has been known as a powerful nonparametric regression method when dealing w...
This paper proposes a classical weighted least squares type of local polynomial smoothing for the an...
When estimating a mean regression function and its derivatives, locally weighted least squares regre...
We investigate the extension of binning methodology to fast computation of several auxiliary quantit...
Local polynomial modelling can be seen as a local fit of the data against a polynomial basis. In thi...
Local polynomial regression is commonly used for estimating regression functions. In practice, howev...
The local polynomial estimator is particularly affected by the curse of dimensionality, which reduce...
Nonparametric regression techniques provide an effective way of identifying and examining structure ...