RECURSIVE ESTIMATION IN SWITCHING AUTOREGRESSIONS WITH A MARKOV REGIME
Copyright policy )RECURSIVE ESTIMATION IN SWITCHING AUTOREGRESSIONS WITH A MARKOV REGIME
Abstract. A hidden Markov regime is a Markov process that governs the time or space dependent distributions of an observed stochastic process. We propose a recursive algorithm for parameter estimation in a switching autoregressive process governed by a hidden Markov chain. A common approach to the recursive estimation problem is to base the estimation on suboptimal modifications of Kalman filtering techniques. The main idea in this paper is to use the maximum likelihood method and from this develop a recursive EM algorithm.
maximum likelihood method, Markov processes: estimation; hidden Markov models, hidden Markov chain, hidden Markov regime, recursive EM algorithm, switching autoregressive process, suboptimal modifications of Kalman filtering, AR(2) models, Gaussian noise, stationarity results, Inference from stochastic processes and prediction
maximum likelihood method, Markov processes: estimation; hidden Markov models, hidden Markov chain, hidden Markov regime, recursive EM algorithm, switching autoregressive process, suboptimal modifications of Kalman filtering, AR(2) models, Gaussian noise, stationarity results, Inference from stochastic processes and prediction
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