Recursive estimation in mixture models with Markov regime

Ulla Holst; Georg Lindgren

Recursive estimation in mixture models with Markov regime

Mathematical Statistics

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Abstract

A recursive algorithm is proposed for estimation of parameters in mixture models, where the observations are governed by a hidden Markov chain. 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.

Original language	English
Pages (from-to)	1683-1690
Journal	IEEE Transactions on Information Theory
Volume	37
Issue number	6
Publication status	Published - 1991

Subject classification (UKÄ)

Probability Theory and Statistics

Free keywords

MIXED DISTRIBUTION
RECURSIVE ML-ESTIMATION
EM-ALGORITHM
MARKOV REGIME

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title = "Recursive estimation in mixture models with Markov regime",

abstract = "A recursive algorithm is proposed for estimation of parameters in mixture models, where the observations are governed by a hidden Markov chain. 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.",

keywords = "MIXED DISTRIBUTION, RECURSIVE ML-ESTIMATION, EM-ALGORITHM, MARKOV REGIME",

author = "Ulla Holst and Georg Lindgren",

year = "1991",

language = "English",

volume = "37",

pages = "1683--1690",

journal = "IEEE Transactions on Information Theory",

issn = "0018-9448",

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T1 - Recursive estimation in mixture models with Markov regime

AU - Holst, Ulla

AU - Lindgren, Georg

PY - 1991

Y1 - 1991

N2 - A recursive algorithm is proposed for estimation of parameters in mixture models, where the observations are governed by a hidden Markov chain. 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.

AB - A recursive algorithm is proposed for estimation of parameters in mixture models, where the observations are governed by a hidden Markov chain. 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.

KW - MIXED DISTRIBUTION

KW - RECURSIVE ML-ESTIMATION

KW - EM-ALGORITHM

KW - MARKOV REGIME

M3 - Article

SN - 0018-9448

VL - 37

SP - 1683

EP - 1690

JO - IEEE Transactions on Information Theory

JF - IEEE Transactions on Information Theory

IS - 6

ER -

Recursive estimation in mixture models with Markov regime

Abstract

Subject classification (UKÄ)

Free keywords

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Cite this