Constrained logarithmic least squares in parameter estimation

This paper first shows that while robust in terms of the average output error, the least squares estimate is sensitive to outliers with respect to the maximum output error. In fact the worst case output error of the least squares can go unbounded. Then, a constrained logarithmic least squares for system identification is proposed. Analytic center algorithms are presented to solve this constrained logarithmic least squares problem.