ROBUST ESTIMATION OF AUTOREGRESSIVE PROCESSES BY FUNCTIONAL LEAST SQUARES.

The stationary autoregressive model but with a long-tailed error distribution is analyzed using the method of functional least squares. A family of estimators indexed by a real parameter is obtained and uniform consistency and weak convergence established. The optimum member of the family is chosen to have minimum variance with respect to the parameter, and the parameter value chosen detects and adjusts for long-tailed error distributions. Results of a simulation are given.