Multiscale autoregressive processes

Abstract

In many applications (e.g. recognition of geophysical and biomedical signals and multiscale analysis of images), it is of interest to analyze and recognize phenomena occuring at different scales. The recently introduced wavelet trans-forms provide a time-and-scale decomposition of signals that offers the possibil-ity of such analysis. At present, however, there is no corresponding statistical framework to support the development of optimal, multiscale statistical sig-nal processing algorithms. In this paper we describe such a framework. The theory of multiscale signal representations leads naturally to models of signals on trees, and this provides the framework for our investigation. In particular, in this paper we describe the class of isotropic processes on homogenous trees and develop a theory of autoregressive models in this context. This leads to generalizations of Schur and Levinson recursions, associated properties of the resulting reflection coefficients, and the initial pieces in a system theory for multiscale modeling. 1M.B. is also with the Centre National de la Recherche Scientifique (CNRS) and A.B. is also wit

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oai:CiteSeerX.psu:10.1.1.899.5940Last time updated on 11/1/2017

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