The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Recursive estimation in switching autoregressions with Markov regime

På svenska

Author

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.

Department/s

Publishing year

1994

Language

English

Pages

489-506

Publication/Series

Journal of Time Series Analysis

Volume

15

Issue

5

Document type

Journal article

Publisher

Wiley-Blackwell

Topic

  • Probability Theory and Statistics

Keywords

  • Switching autoregressions
  • Markov regime
  • recursive estimation
  • EM algorithm

Status

Published

Research group

  • Spatio-Temporal Stochastic Modelling Group

ISBN/ISSN/Other

  • ISSN: 0143-9782