Maximum likelihood parameter estimation with a min/Max criterion

Statistical methods for estimating the parameters of a system are often based on assuming that the system inputs are Gaussian. As a result least-squares criteria are commonly used for estimating the system parameters. In this paper we will describe methods that are based on uniformly distributed system inputs. The estimates of the system parameters will then be found from min/max operations on linear combinations of the output samples. A new method for solving the resulting min/max problems is described and the min/max criterion is used in an application where it performs better than the commonly used least-squares criterion.