AbstractWe suggest a method for reducing variance in nonparametric surface estimation. The technique is applicable to a wide range of inferential problems, including both density estimation and regression, and to a wide variety of estimator types. It is based on estimating the contours of a surface by minimising deviations of elementary surface estimates along a quadratic curve. Once a contour estimate has been obtained, the final surface estimate is computed by averaging conventional surface estimates along a portion of the contour. Theoretical and numerical properties of the technique are discussed
A technique is suggested for reducing the order of bias of kernel estimators by weighting the contri...
A transformation [phi] defined on a subset E of the real line and taking real values reduces the var...
In this paper, we discuss estimation of bivariate jump regression functions. An a.s. consistent esti...
We suggest a method for reducing variance in nonparametric surface estimation. The technique is appl...
Nonparametric inference for functional data over two-dimensional domains entails additional computat...
Estimation of the level sets for an unknown probability density is done with no specific assumed for...
We develop a variance reduction method for smoothing splines. For a given point of estimation, we de...
This thesis is concerned with statistical modelling techniques which involve nonpara- metric smoothi...
This paper is the second of a short series of articles aimed towards describing some of the various ...
A simple algorithm for estimating the regression function over the United States is introduced. The ...
A new method for bias reduction in nonparametric density estimation is proposed. The method is a sim...
Abstract In this article, we present and discuss three statistical methods for Surface Reconstructio...
Traditional response surface methodology focuses on modeling responses using parametric models with ...
A new method for bias reduction in nonparametric density estimation is proposed. The method is a sim...
In this article, we present and discuss three statistical methods for Surface Reconstruction. A typi...
A technique is suggested for reducing the order of bias of kernel estimators by weighting the contri...
A transformation [phi] defined on a subset E of the real line and taking real values reduces the var...
In this paper, we discuss estimation of bivariate jump regression functions. An a.s. consistent esti...
We suggest a method for reducing variance in nonparametric surface estimation. The technique is appl...
Nonparametric inference for functional data over two-dimensional domains entails additional computat...
Estimation of the level sets for an unknown probability density is done with no specific assumed for...
We develop a variance reduction method for smoothing splines. For a given point of estimation, we de...
This thesis is concerned with statistical modelling techniques which involve nonpara- metric smoothi...
This paper is the second of a short series of articles aimed towards describing some of the various ...
A simple algorithm for estimating the regression function over the United States is introduced. The ...
A new method for bias reduction in nonparametric density estimation is proposed. The method is a sim...
Abstract In this article, we present and discuss three statistical methods for Surface Reconstructio...
Traditional response surface methodology focuses on modeling responses using parametric models with ...
A new method for bias reduction in nonparametric density estimation is proposed. The method is a sim...
In this article, we present and discuss three statistical methods for Surface Reconstruction. A typi...
A technique is suggested for reducing the order of bias of kernel estimators by weighting the contri...
A transformation [phi] defined on a subset E of the real line and taking real values reduces the var...
In this paper, we discuss estimation of bivariate jump regression functions. An a.s. consistent esti...