Estimation of parameters from quantized noisy observations

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.