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Wavelet Based Estimation for Univariate Stable Laws
 

Summary: Wavelet Based Estimation for Univariate
Stable Laws
Anestis Antoniadis
Laboratoire IMAG-LMC, University Joseph Fourier,
BP 53, 38041 Grenoble Cedex 9, France
Andrey Feuerverger
Department of Statistics, University of Toronto,
Toronto, Ontario, M5S 3G3 Canada
and
Paulo Gon¸calves
INRIA Rh^one-Alpes, ZIRST, 655 Avenue de l'Europe,
38330 Monbonnot Saint Martin, France.
Abstract
Stable distributions are characterized by four parameters which can be estimated
via a number of methods, and although approximate maximum likelihood estimation
techniques have been proposed, they are computationally intensive and difficult to
implement. This article describes a fast, wavelet-based, regression-type method for
estimating the parameters of a stable distribution. Fourier domain representations,
combined with a wavelet multiresolution approach, are shown to be effective and
highly efficient tools for inference in stable law families. Our procedures are

  

Source: Antoniadis, Anestis - Laboratoire Jean Kuntzmann, Université Joseph Fourier

 

Collections: Mathematics