The Wavelet Transform in Regression
Joanna Trzęsiok · 2009
A 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.