Abstract. Local polynomial regression is extremely popular in applied settings. Recent developments in shape constrained nonparametric regression allow practitioners to impose constraints on local polynomial estimators thereby ensuring that the resulting estimates are consistent with underlying theory. However, it turns out that local polynomial derivative estimates may fail to coincide with the analytic derivative of the local polynomial regression estimate which can be problematic, particularly in the context of shape constrained estima-tion. In such cases practitioners might prefer to instead use analytic derivatives along the lines of those proposed in the local constant setting by Rilstone & Ullah (1989). Demon-strations and applic...
Local polynomial fitting has been known as a powerful nonparametric regression method when dealing w...
We consider the estimation of multivariate regression functions r(x1,...,xd) and their partial deriv...
Local polynomial estimators are popular techniques for nonparametric regression estimation and have ...
We present a fully automated framework to estimate derivatives nonparametrically without estimating ...
We present a fully automated framework to estimate derivatives nonparametrically without esti-mating...
Nonparametric derivative estimation has never attracted much attention as one gets the derivative es...
Abstract: Nonparametric derivative estimation has never attracted much atten-tion as one gets the de...
Data-analytic approaches to regression problems, arising from many scientific disciplines are descri...
AbstractNonparametric regression estimator based on locally weighted least squares fitting has been ...
This thesis examines local polynomial regression. Local polynomial regression is one of non-parametr...
summary:Local polynomials are used to construct estimators for the value $m(x_{0})$ of the regressio...
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...
We propose a modi cation of local polynomial time series regression estimators that improves ef ci...
Nonparametric regression with long-range, short-range and antipersistent errors is considered. Local...
Local polynomial fitting has been known as a powerful nonparametric regression method when dealing w...
We consider the estimation of multivariate regression functions r(x1,...,xd) and their partial deriv...
Local polynomial estimators are popular techniques for nonparametric regression estimation and have ...
We present a fully automated framework to estimate derivatives nonparametrically without estimating ...
We present a fully automated framework to estimate derivatives nonparametrically without esti-mating...
Nonparametric derivative estimation has never attracted much attention as one gets the derivative es...
Abstract: Nonparametric derivative estimation has never attracted much atten-tion as one gets the de...
Data-analytic approaches to regression problems, arising from many scientific disciplines are descri...
AbstractNonparametric regression estimator based on locally weighted least squares fitting has been ...
This thesis examines local polynomial regression. Local polynomial regression is one of non-parametr...
summary:Local polynomials are used to construct estimators for the value $m(x_{0})$ of the regressio...
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...
We propose a modi cation of local polynomial time series regression estimators that improves ef ci...
Nonparametric regression with long-range, short-range and antipersistent errors is considered. Local...
Local polynomial fitting has been known as a powerful nonparametric regression method when dealing w...
We consider the estimation of multivariate regression functions r(x1,...,xd) and their partial deriv...
Local polynomial estimators are popular techniques for nonparametric regression estimation and have ...