DocumentCode
3079847
Title
AR(∞) estimation and nonparametric stochastic complexity
Author
Gerencsér, László
Author_Institution
Dept. of Electr. Eng., McGill Univ., Montreal, Que., Canada
fYear
1990
fDate
5-7 Dec 1990
Firstpage
829
Abstract
Let H * be the transfer function of a linear stochastic system such that H * and its inverse are in H ∞( D ). Writing the system as an AR(∞) system, the best AR( k ) approximation of the system is estimated using the method of least squares. Then the effect of undermodeling and parameter uncertainty (due to estimation) on prediction, and the optimal choice of k are investigated. The result is applied to the AR approximation of ARMA-systems
Keywords
least squares approximations; parameter estimation; statistical analysis; stochastic processes; stochastic systems; transfer functions; ARMA-systems; least squares approximation; linear stochastic system; nonparametric stochastic complexity; parameter estimation; parameter uncertainty; transfer function; Control systems; Equations; H infinity control; Information theory; Polynomials; Stochastic processes; Stochastic systems; Transfer functions; Upper bound; Visualization;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 1990., Proceedings of the 29th IEEE Conference on
Conference_Location
Honolulu, HI
Type
conf
DOI
10.1109/CDC.1990.203704
Filename
203704
Link To Document