Parameter estimation from noisy measurements

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Abstract
This article considers the problem of estimating linear model parameters from noisy measurements. The starting point is the classical approach by Koopmans for linear regression analysis. It is known that concerning the direct application of those early results for process identification, neither the original Koopmans algorithm nor its updated forms called Koopmans–Levin algorithms exhibit maximum-likelihood (ML) parameter estimation. In this article, a new, numerically advanced method is developed to ensure ML property for the parameter estimation, assuming noisy inputs and outputs, respectively.