Recursive estimation in switching autoregressions with Markov regime
Författare
Summary, in English
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.
Avdelning/ar
- Matematisk statistik
- Spatio-Temporal Stochastic Modelling Group
Publiceringsår
1994
Språk
Engelska
Sidor
489-506
Publikation/Tidskrift/Serie
Journal of Time Series Analysis
Volym
15
Issue
5
Länkar
Dokumenttyp
Artikel i tidskrift
Förlag
Wiley-Blackwell
Ämne
- Probability Theory and Statistics
Nyckelord
- Switching autoregressions
- Markov regime
- recursive estimation
- EM algorithm
Status
Published
Forskningsgrupp
- Spatio-Temporal Stochastic Modelling Group
ISBN/ISSN/Övrigt
- ISSN: 0143-9782