Add to ORKGA bstract The wavelet transform was introduced in the 1980’s and it was developed as an alternative to the short time Fourier transform. The wavelets theory is very popular in signal processing and pattern recognition and its applications are still growing. This paper presents the wavelet transform in nonparametric regression. The use o f wavelets in statistical applications was pioneered by D. Donoho and I. Johnstone. Here we discuss their methodology- wavelet shrinkage. The wavelet transform is compared with another nonparametric regression method- splines
This thesis is concerned with distribution theory as well as hypothesis testing and inference for wa...
. Various aspects of the wavelet approach to nonparametric regression are considered, with the overa...
A powerful and efficient method for nonparametric regression involves taking the discrete wavelet tr...
The wavelet transform was introduced in the 19S0's and it was developed as an alternative in tin sho...
Wavelet regression is a new nonparametric regression approach. Com-pared with traditional methods, w...
Wavelet analysis has been found to be a powerful tool for the nonparametric estimation of spatially-...
International audienceWavelet analysis has been found to be a powerful tool for the nonparametric es...
This paper provides a tutorial, largely based on Nason and Silverman (1994), on the use of the discr...
In recent years there has been a considerable development in the use of wavelet methods in statistic...
International audienceThe development of wavelet theory has in recent years spawned applications in ...
Vita.Two research areas that have generated a great deal of interest in the field of statistics are ...
Introduction We congratulate the three authors for their thought-provoking and original work. We th...
In this paper we give the main uses of wavelets in statistics, with emphasis in time series analysis...
In this article, a nonparametric regression problem is discussed on wavelet bases via a Bayesian str...
This thesis is concerned with distribution theory as well as hypothesis testing and inference for wa...
This thesis is concerned with distribution theory as well as hypothesis testing and inference for wa...
. Various aspects of the wavelet approach to nonparametric regression are considered, with the overa...
A powerful and efficient method for nonparametric regression involves taking the discrete wavelet tr...
The wavelet transform was introduced in the 19S0's and it was developed as an alternative in tin sho...
Wavelet regression is a new nonparametric regression approach. Com-pared with traditional methods, w...
Wavelet analysis has been found to be a powerful tool for the nonparametric estimation of spatially-...
International audienceWavelet analysis has been found to be a powerful tool for the nonparametric es...
This paper provides a tutorial, largely based on Nason and Silverman (1994), on the use of the discr...
In recent years there has been a considerable development in the use of wavelet methods in statistic...
International audienceThe development of wavelet theory has in recent years spawned applications in ...
Vita.Two research areas that have generated a great deal of interest in the field of statistics are ...
Introduction We congratulate the three authors for their thought-provoking and original work. We th...
In this paper we give the main uses of wavelets in statistics, with emphasis in time series analysis...
In this article, a nonparametric regression problem is discussed on wavelet bases via a Bayesian str...
This thesis is concerned with distribution theory as well as hypothesis testing and inference for wa...
This thesis is concerned with distribution theory as well as hypothesis testing and inference for wa...
. Various aspects of the wavelet approach to nonparametric regression are considered, with the overa...
A powerful and efficient method for nonparametric regression involves taking the discrete wavelet tr...