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We derive the minimum entropy estimation (MEE) method from information theory to show the similarity of this method to the maximum likelihood method for the linear regression problem. The result is a nonparametric-based identification technique that can be applied in any case with iid noise that outperforms estimators in this case, including the popular LS method and a recently-developed (and limited) version of the MEE. Performance-wise, the MEE method is comparable to the expectation-maximization (EM) method. Its application to FIR system identification produces a very efficient implementation of this technique.
Date of Conference: 17-21 May 2004