A wavelet basis selection procedure is presented for wavelet regression. Both the basis and the threshold are selected using cross-validation. The method includes the capability of incorporating prior knowledge on the smoothness (or shape of the basis functions) into the basis selection procedure. The results of the method are demonstrated on sampled functions widely used in the wavelet regression literature. The results of the method are contrasted with other published methods
The core of the wavelet approach to nonparametric regression is thresholding of wavelet coefficients...
Vita.Two research areas that have generated a great deal of interest in the field of statistics are ...
The wavelet packet transform (WPT) [1] is an extension of the discrete wavelet transform (DWT). The ...
In wavelet regression, choosing threshold value is a crucial issue. A too large value cuts too many ...
This paper is about using wavelets for regression. The main aim of the paper is to introduce and dev...
. Various aspects of the wavelet approach to nonparametric regression are considered, with the overa...
In this paper we discuss how to use wavelet decompositions to select a regression model. The methodo...
Wavelets are being suggested as a platform for various tasks in image processing. The advantage of w...
Wavelets are being suggested as a platform for various tasks in image processing. The advantage of w...
Wavelet shrinkage methods are widely recognized as a useful tool for non-parametric regression and s...
Wavelet shrinkage methods are widely recognized as a useful tool for non-parametric regression and s...
In Oh, Naveau and Lee (2001) a simple method is proposed for reducing the bias at the boundaries for...
The core of the wavelet approach to nonparametric regression is thresholding of wavelet coefficients...
Representation or compression of data sets in the wavelet space is usually performed to retain the m...
We consider the problem of estimating the relationship between a response variable and a set of expl...
The core of the wavelet approach to nonparametric regression is thresholding of wavelet coefficients...
Vita.Two research areas that have generated a great deal of interest in the field of statistics are ...
The wavelet packet transform (WPT) [1] is an extension of the discrete wavelet transform (DWT). The ...
In wavelet regression, choosing threshold value is a crucial issue. A too large value cuts too many ...
This paper is about using wavelets for regression. The main aim of the paper is to introduce and dev...
. Various aspects of the wavelet approach to nonparametric regression are considered, with the overa...
In this paper we discuss how to use wavelet decompositions to select a regression model. The methodo...
Wavelets are being suggested as a platform for various tasks in image processing. The advantage of w...
Wavelets are being suggested as a platform for various tasks in image processing. The advantage of w...
Wavelet shrinkage methods are widely recognized as a useful tool for non-parametric regression and s...
Wavelet shrinkage methods are widely recognized as a useful tool for non-parametric regression and s...
In Oh, Naveau and Lee (2001) a simple method is proposed for reducing the bias at the boundaries for...
The core of the wavelet approach to nonparametric regression is thresholding of wavelet coefficients...
Representation or compression of data sets in the wavelet space is usually performed to retain the m...
We consider the problem of estimating the relationship between a response variable and a set of expl...
The core of the wavelet approach to nonparametric regression is thresholding of wavelet coefficients...
Vita.Two research areas that have generated a great deal of interest in the field of statistics are ...
The wavelet packet transform (WPT) [1] is an extension of the discrete wavelet transform (DWT). The ...