A hidden Markov regime is a Markov process that governs the time or spacedependent distributions of an observed stochastic process. In the studied model, a hidden Markov chain governs the distribution of a mixed autoregressive process. This paper deals with simultaneous recursive parameter estimation and reconstruction of the hidden Markov chain. The authors use a MAP estimate for the reconstruction of the Markov chain together with a recursive EM-algorithm for the parameters. Simulations are made in models with two and three Markov states. An attempt has been made to fit real data from a clock striking twelve to the model. This paper also includes an example of robust estimation for data with clustered outliers, where one Markov state represents the outliers among the observations.
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