PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Karakus O., Kuruoglu E. E., Altinkaya M. A. Estimation of the nonlinearity degree for polynomial autoregressive processes with RJMCMC. In: EUSIPCO 215 - European Signal Processing Conference (Nice, France, 31 August 2015). Proceedings, pp. 958 - 962. IEEE, 2015.
 
 
Abstract
(English)
Despite the popularity of linear process models in signal and image processing, various real life phenomena exhibit nonlinear characteristics. Compromising between the realistic and computationally heavy nonlinear models and the simplicity of linear estimation methods, linear in the parameters nonlinear models such as polynomial autoregressive (PAR) models have been accessible analytical tools for modelling such phenomena. In this work, we aim to demonstrate the potentials of Reversible Jump Markov Chain Monte Carlo (RJMCMC) which is a successful statistical tool in model dimension estimation in nonlinear process identification. We explore the capability of RJMCMC in jumping not only between spaces with dif- ferent dimensions, but also between different classes of models. In particular, we demonstrate the success of RJMCMC in sampling in linear and nonlinear spaces of varying dimensions for the estimation of PAR processes.
URL: http://www.eurasip.org/index.php?option=com_content&view=article&id=80&Itemid=1089
Subject Polynomial autoregressive process
Reversible Jump MCMC
PAR model
Bayesian estimation
Nonlinear process
Nonlinearity degree estimation
G.3 PROBABILITY AND STATISTICS. Stochastic processes
G.3 PROBABILITY AND STATISTICS. Probabilistic algorithms (including Monte Carlo)
G.3 PROBABILITY AND STATISTICS. Time series analysis
62J02 General nonlinear regression
65C05 Monte Carlo methods
65C40 Computational Markov chains
62F15 Bayesian inference


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