Stochastic approximation methods are used to generate a sequence of "$M$-estimates" for the unknown parameters of an autoregressive process of known, finite order which may have heavy-tailed innovations. Weak dependence properties, which can be demonstrated for many autoregressive processes, are used in the proof that the sequence converges almost surely to the parameters. A brief Monte Carlo study verifies that bounded influence functions provide protection for recursive procedures against heavy-tailed innovations.
Katherine Campbell. "Recursive Computation of $M$-Estimates for the Parameters of a Finite Autoregressive Process." Ann. Statist. 10 (2) 442 - 453, June, 1982. https://doi.org/10.1214/aos/1176345785