Title :
Estimation of parameters from quantized noisy observations
Author :
Finesso, L. ; Gerencser, L. ; Kmecs, I.
Author_Institution :
Inst. of Syst. Sci. & Biomed. Eng., LADSEB, Padua, Italy
fDate :
Aug. 31 1999-Sept. 3 1999
Abstract :
The purpose of this paper is to formulate and study the problem of system identification with Gaussian noise and quantized observations. The prime examples that we study are Gaussian AR(1)-systems and the simplest Gaussian linear regression. The main results of the paper are the development of a randomization technique for the effective solution of the likelihood equation and computational experiments to demonstrate the paradoxical role of noise.
Keywords :
Gaussian noise; identification; parameter estimation; regression analysis; Gaussian AR(1)-systems; Gaussian linear regression; Gaussian noise; likelihood equation; parameter estimation; quantized noisy observations; randomization technique; system identification; Approximation methods; Linear regression; Mathematical model; Maximum likelihood estimation; Noise; Noise measurement; Hidden Markov Models; Linear regression models; Metropolis method; maximum likelihood estimation; stochastic approximation;
Conference_Titel :
Control Conference (ECC), 1999 European
Conference_Location :
Karlsruhe
Print_ISBN :
978-3-9524173-5-5
