Articolo in rivista, 2009, ENG, 10.1016/j.dsp.2007.11.004
Salas-Gonzalez D.; Kuruoglu E. E.; Ruiz D. P.
University of Granada, Granada, Spagna; CNR-ISTI, Pisa, Italy; University of Granada, Granada, Spagna
Over the last decades, the ±-stable distribution has proved to be a very efficient model for impulsive data. In this paper, we propose an extension of stable distributions, namely mixture of ±-stable distributions to model multimodal, skewed and impulsive data. A fully Bayesian framework is presented for the estimation of the stable density parameters and the mixture parameters. As opposed to most previous work on mixture models, the model order is assumed unknown and is estimated using reversible jump Markov chain Monte Carlo. It is important to note that the Gaussian mixture model is a special case of the presented model which provides additional flexibility to model skewed and impulsive phenomena. The algorithm is tested using synthetic and real data, accurately estimating ±-stable parameters, mixture coefficients and the number of components in the mixture.
Digital signal processing (Print) 19 (2), pp. 250–264
Bayesian estimation, Stable distributions, mixture distributions, MCMC
ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
CNR authors
External links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
DOI: 10.1016/j.dsp.2007.11.004
URL: http://www.sciencedirect.com/science/article/pii/S1051200407001819
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:44279
DOI: 10.1016/j.dsp.2007.11.004
ISI Web of Science (WOS): 000263213700009
Scopus: 2-s2.0-58549094349