Local polynomial regression is a generalization of local mean smoothing as described by Nadaraya (1964) and Watson (1964). Instead of fitting a local mean, one instead fits a local pth-order polynomial. Calculations for local polynomial regression are naturally more complex than those for local means, but local polynomial smooths have better statistical properties. The computational complexity may, however, be alleviated by using a Stata plugin. In this article, we describe the locpoly command for performing local polynomial regression. The calculations involved are implemented in both ado-code and with a plugin, allowing the user to assess the speed improvement obtained from using the plugin. Source code for the plugin is also provided as ...
Local polynomial regression is a useful non-parametric regression tool to explore fine data structur...
Nonparametric estimators, local polynomial fitting, autoregressive process, 62G07, 62H12, 62M09,
This paper proves that local polynomial regression smoothers achieve linear minimax efficiency over ...
Local polynomial regression is a generalization of local mean smoothing as described by Nadaraya (19...
This thesis examines local polynomial regression. Local polynomial regression is one of non-parametr...
We investigate the extension of binning methodology to fast computation of several auxiliary quantit...
Data-analytic approaches to regression problems, arising from many scientific disciplines are descri...
Abstract: We propose a method for incorporating variable selection into local polynomial regression....
We consider local polynomial fitting for estimating a regression function and its derivatives nonpar...
We consider local polynomial fitting for estimating a regression function and its derivatives nonpar...
Abstract: There has been much justifiable recent interest in local polynomial regression, and in par...
The local least-squares estimator for a regression curve cannot provide optimal results when non-Gau...
When estimating a regression function or its derivatives, local polynomials are an attractive choice...
This paper proposes a classical weighted least squares type of local polynomial smoothing for the an...
Nonparametric regression with long-range, short-range and antipersistent errors is considered. Local...
Local polynomial regression is a useful non-parametric regression tool to explore fine data structur...
Nonparametric estimators, local polynomial fitting, autoregressive process, 62G07, 62H12, 62M09,
This paper proves that local polynomial regression smoothers achieve linear minimax efficiency over ...
Local polynomial regression is a generalization of local mean smoothing as described by Nadaraya (19...
This thesis examines local polynomial regression. Local polynomial regression is one of non-parametr...
We investigate the extension of binning methodology to fast computation of several auxiliary quantit...
Data-analytic approaches to regression problems, arising from many scientific disciplines are descri...
Abstract: We propose a method for incorporating variable selection into local polynomial regression....
We consider local polynomial fitting for estimating a regression function and its derivatives nonpar...
We consider local polynomial fitting for estimating a regression function and its derivatives nonpar...
Abstract: There has been much justifiable recent interest in local polynomial regression, and in par...
The local least-squares estimator for a regression curve cannot provide optimal results when non-Gau...
When estimating a regression function or its derivatives, local polynomials are an attractive choice...
This paper proposes a classical weighted least squares type of local polynomial smoothing for the an...
Nonparametric regression with long-range, short-range and antipersistent errors is considered. Local...
Local polynomial regression is a useful non-parametric regression tool to explore fine data structur...
Nonparametric estimators, local polynomial fitting, autoregressive process, 62G07, 62H12, 62M09,
This paper proves that local polynomial regression smoothers achieve linear minimax efficiency over ...