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

fYear

1999

fDate

Aug. 31 1999-Sept. 3 1999

Firstpage

1648

Lastpage

1653

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (ECC), 1999 European

Conference_Location

Karlsruhe

Print_ISBN

978-3-9524173-5-5

Type

conf

Filename

7099550

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=706606