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Markov chain models, time series analysis and extreme value theory

Part of: Markov processes Inference from stochastic processes

Published online by Cambridge University Press:  01 July 2016

D. S. Poskitt  and
Shin-Ho Chung
D. S. Poskitt*
Affiliation:
Australian National University
Shin-Ho Chung*
Affiliation:
Australian National University
*
∗ Postal address: Department of Statistics, Australian National University, Canberra ACT 0200, Australia.
∗∗ Postal address: Department of Chemistry, Australian National University, Canberra ACT 0200, Australia.
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Abstract

Markov chain processes are becoming increasingly popular as a means of modelling various phenomena in different disciplines. For example, a new approach to the investigation of the electrical activity of molecular structures known as ion channels is to analyse raw digitized current recordings using Markov chain models. An outstanding question which arises with the application of such models is how to determine the number of states required for the Markov chain to characterize the observed process. In this paper we derive a realization theorem showing that observations on a finite state Markov chain embedded in continuous noise can be synthesized as values obtained from an autoregressive moving-average data generating mechanism. We then use this realization result to motivate the construction of a procedure for identifying the state dimension of the hidden Markov chain. The identification technique is based on a new approach to the estimation of the order of an autoregressive moving-average process. Conditions for the method to produce strongly consistent estimates of the state dimension are given. The asymptotic distribution of the statistic underlying the identification process is also presented and shown to yield critical values commensurate with the requirements for strong consistency.

Keywords

MARKOV CHAINAUTOREGRESSIVE MOVING-AVERAGEREALIZATIONIDENTIFICATIONCONSISTENCYEXTREME VALUE

MSC classification

Primary: 62M10: Time series, auto-correlation, regression, etc.
Secondary: 62M05: Markov processes 60J10: Markov chains (discrete-time Markov processes on discrete state spaces)
Type
General Applied Probability
Information
Advances in Applied Probability , Volume 28 , Issue 2 , June 1996 , pp. 405 - 425
Copyright
Copyright © Applied Probability Trust 1996 

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Footnotes

This work was supported in part by a grant from the National Health & Medical Research Council of Australia.

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