Robust system‐parameter identification: The influence functional approach

Abstract The influence functional, developed by Martin and Yohai, is an asymptotic robustness measure of a parameter estimate's sensitivity to the infinitesimal occurrence of correlated outlier contamination of a measurement sequence. Here, the usefulness of the influence functional as a tool for characterizing estimator robustness in system parameter identification is explored. In particular, this utility is illustrated by examining the influence functional as a measure of robustness for the choice of the error‐shaping function in the correlation or instrumental variables approach to system parameter identification.