Treffer: Maximum a posteriori estimation of time-varying ARMA processes from noisy observations
Title:
Maximum a posteriori estimation of time-varying ARMA processes from noisy observations
Authors:
Source:
IEEE Transactions on Acoustics, Speech, and Signal Processing. 36:471-476
Publisher Information:
Institute of Electrical and Electronics Engineers (IEEE), 1988.
Publication Year:
1988
Subject Terms:
time-dependent, Estimation and detection in stochastic control theory, Time series, auto-correlation, regression, etc. in statistics (GARCH), Identification in stochastic control theory, Computational methods in stochastic control, 0202 electrical engineering, electronic engineering, information engineering, Probabilistic methods, stochastic differential equations, 02 engineering and technology, 0101 mathematics, 01 natural sciences
Document Type:
Fachzeitschrift
Article
File Description:
application/xml
ISSN:
0096-3518
DOI:
10.1109/29.1551
Rights:
IEEE Copyright
Accession Number:
edsair.doi.dedup.....54328384b17ed5988dcac729bf1bbd2c
Database:
OpenAIRE
Weitere Informationen
Summary: We consider the estimation of the parameters of discrete-time ARMA processes observed in white noise. A class of time-varying ARMA processes, in which the parameter process is the output of a known linear system driven by white Gaussian noise is considered. The maximum a posteriori (MAP) estimator is defined for the trajectory of the parameter's random process. An EM-based iterative algorithm is derived. The posterior probability of the parameters is increased in each iteration, and convergence to stationary points of the posterior probability is guaranteed. Each iteration involves two linear systems and is easily implemented. It is shown that similar results can be obtained for a wide class of parameter estimation problems.