Modified maximum likelihood method for the robust estimation of system parameters from very noisy data

Abstract

When experiments are conducted there is always a chance of the occurrence of large measurement errors (outliers). Common identification methods like generalized least squares, maximum likelihood etc. may not converge in these situations due to the presence of outliers. Here we present a method for the robust estimation of system parameters based on the censoring of data and employing the maximum likelihood estimation. Several simulated examples show that the modified maximum likelihood method works well in situations where other methods failed.