Title :
Wavelet-based Bayesian analysis of generalized long-memory process
Author :
Gonzaga, Alex ; Kawanaka, Akira
Author_Institution :
Dept. of Electr. & Electron. Eng., Sophia Univ., Tokyo, Japan
Abstract :
In this paper we propose a Bayesian approach to estimating the parameters and predicting future values of a generalized long-memory process utilizing the approximate likelihood function of discrete wavelet packet coefficients. This approximation does not depend on the length of the signal, but the length of the wavelet filter, which is under the control of the analyst. We illustrate our approach by an example applying simulated data.
Keywords :
Bayes methods; approximation theory; filtering theory; parameter estimation; prediction theory; wavelet transforms; approximate likelihood function; discrete wavelet packet coefficients; future value prediction; generalized long-memory process; parameter estimation; wavelet filter length; wavelet-based Bayesian analysis; Autocorrelation; Autoregressive processes; Bayesian methods; Discrete wavelet transforms; Filters; Frequency; Parameter estimation; Wavelet analysis; Wavelet packets; White noise;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05). IEEE International Conference on
Print_ISBN :
0-7803-8874-7
DOI :
10.1109/ICASSP.2005.1416007
