DocumentCode
2160124
Title
Likelihood based uncertainty bounding in prediction error identification using ARX models: A simulation study
Author
den Dekker, Arnold J. ; Bombois, Xavier ; Van den Hof, Paul M. J.
Author_Institution
Delft Center for Syst. & Control, Delft Univ. of Technol., Delft, Netherlands
fYear
2007
fDate
2-5 July 2007
Firstpage
2879
Lastpage
2886
Abstract
The purpose of this paper is to evaluate the reliability and finite sample properties of different likelihood based methods for constructing probabilistic parameter confidence regions in prediction error identification using ARX (Auto Regression with eXogenous inputs) models. The paper presents alternatives for the “classical” approach to constructing probabilistic confidence regions in prediction error identification.
Keywords
autoregressive processes; maximum likelihood estimation; prediction theory; probability; reliability theory; uncertain systems; ARX models; auto regression with exogenous inputs models; finite sample properties; likelihood based methods; likelihood based uncertainty bounding; prediction error identification; probabilistic confidence regions; probabilistic parameter confidence regions; reliability; Data models; Maximum likelihood estimation; Predictive models; Transfer functions; Vectors; White noise;
fLanguage
English
Publisher
ieee
Conference_Titel
Control Conference (ECC), 2007 European
Conference_Location
Kos
Print_ISBN
978-3-9524173-8-6
Type
conf
Filename
7068516
Link To Document