Wavelet transforms originated in geophysics in the early 1980s for the analysis of seismic
signals. Since then, significant mathematical advances in wavelet theory have enabled a …

Reviews a significant component of the rich field of statistical multiresolution (MR) modeling
and processing. These MR methods have found application and permeated the literature of …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

The embedded zerotree wavelet algorithm (EZW) is a simple, yet remarkably effective,
image compression algorithm, having the property that the bits in the bit stream are …

A simple, nonrigorous, synthetic view of wavelet theory is presented for both review and
tutorial purposes. The discussion includes nonstationary signal analysis, scale versus …

A comprehensive, step-by-step introduction to wavelets in statistics. What are wavelets?
What makes them increasingly indispensable in statistical nonparametrics? Why are they …

Multiscale principal‐component analysis (MSPCA) combines the ability of PCA to
decorrelate the variables by extracting a linear relationship with that of wavelet analysis to …

Fractional Brownian motion (FBM) offers a convenient modeling for nonstationary stochastic
processes with long-term dependencies and 1/f-type spectral behavior over wide ranges of …

This paper discusses techniques for generating digital sequences of noise which simulate
processes with certain known properties or describing equations. Part I of the paper …

We develop a new multiscale modeling framework for characterizing positive-valued data
with long-range-dependent correlations (1/f noise). Using the Haar wavelet transform and a …