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Recursive estimation in mixture models with Markov regime

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2 Author(s)
Holst, U. ; Dept. of Math. Stat., Lund Univ., Sweden ; Lindgren, G.

A recursive algorithm is proposed for estimation of parameters in mixture models, where the observations are governed by a hidden Markov chain. The often badly conditioned information matrix is estimated, and its inverse is incorporated into the algorithm. The performance of the algorithm is studied by simulations of a symmetric normal mixture. The algorithm seems to be stable and produce approximately normally distributed estimates, provided the adaptive matrix is kept well conditioned. Some numerical examples are included

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Information Theory, IEEE Transactions on  (Volume:37 ,  Issue: 6 )