PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Salas-Gonzalez D., Kuruoglu E. E., Ruiz D. P. Estimation of mixtures of skewed alpha stable processes with unknown number of components. In: European Signal Processing Conference (Firenze, Italia, 4-8 Settembre 2006). Proceedings, vol. 1 EUSIPCO, 2006.
 
 
Abstract
(English)
Alpha stable distributions are widely accepted models for impulsive data. Despite their flexibility in modelling varying degrees of impulsiveness and skewness, they fall short of modelling multimodal data. In this work, we present the alpha-stable mixture model which provides a framework for modelling multimodal, skewed and impulsive data. We describe new parameter estimation techniques for this model based on numerical Bayesian techniques which not only can estimate the alpha-stable and mixture parameters, but also the number of components in the mixture. In particular, we employ the reversible jump Markov chain Monte Carlo technique.
URL: http://www.eusipco2006.org
Subject Alpha-stable distribution
Mixture distributions
Reversible jump Markov chain Monte Carlo
G.3 Probability and Statistics. Distribution functions
G.3 Probability and Statistics. Probabilistic algorithms (including MonteCarlo)


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