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Hurst Index of Functions of Long-Range-Dependent Markov Chains

Published online by Cambridge University Press:  04 February 2016

Barlas Oğuz*
Affiliation:
University of California, Berkeley
Venkat Anantharam*
Affiliation:
University of California, Berkeley
*
Postal address: Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA.
Postal address: Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA.
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Abstract

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A positive recurrent, aperiodic Markov chain is said to be long-range dependent (LRD) when the indicator function of a particular state is LRD. This happens if and only if the return time distribution for that state has infinite variance. We investigate the question of whether other instantaneous functions of the Markov chain also inherit this property. We provide conditions under which the function has the same degree of long-range dependence as the chain itself. We illustrate our results through three examples in diverse fields: queueing networks, source compression, and finance.

Type
Research Article
Copyright
© Applied Probability Trust 

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