Status: Bibliographieeintrag
Standort: ---
Exemplare:
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| Online-Ressource |
Verfasst von: | Kulik, Rafal [VerfasserIn]  |
| Wichelhaus, Cornelia [VerfasserIn]  |
Titel: | Dependence in lag for Markov chains on partially ordered state spaces with applications to degradable networks |
Verf.angabe: | Rafał Kulik, Cornelia Wichelhaus |
Umfang: | 14 S. |
Fussnoten: | Gesehen am 30.05.2018 |
Titel Quelle: | Enthalten in: Stochastic models |
Jahr Quelle: | 2007 |
Band/Heft Quelle: | 23(2007), 4, S. 683-696 |
ISSN Quelle: | 1532-4214 |
Abstract: | We study the property of dependence in lag for Markov chains on countable partially ordered state spaces and give conditions which ensure that a process is monotone in lag. In case of linearly ordered state spaces, proofs are based on the Lorentz inequality. However, we show that on partially ordered spaces Lorentz inequality is only true under additional assumptions. By using supermodular-type stochastic orders we derive comparison inequalities that compare the internal dependence structure of processes with that of their speeding-down versions. Applications of the results are presented for degradable exponential networks in which the nodes are subject to random breakdowns and repairs. We obtain comparison results for the breakdown processes as well as for the queue length processes that are not even Markovian on their own. |
DOI: | doi:10.1080/15326340701646007 |
URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
Verlag: http://dx.doi.org/10.1080/15326340701646007 |
| DOI: https://doi.org/10.1080/15326340701646007 |
Datenträger: | Online-Ressource |
Sprache: | eng |
K10plus-PPN: | 1575865807 |
Verknüpfungen: | → Zeitschrift |
Dependence in lag for Markov chains on partially ordered state spaces with applications to degradable networks / Kulik, Rafal [VerfasserIn] (Online-Ressource)
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