Data-analytic and monitoring schemes for a class of discrete point processes.
| dc.contributor.advisor | Neuts, Marcel F. | en_US |
| dc.contributor.author | Chandramouli, Yegnanarayanan. | |
| dc.creator | Chandramouli, Yegnanarayanan. | en_US |
| dc.date.accessioned | 2011-10-31T17:35:03Z | |
| dc.date.available | 2011-10-31T17:35:03Z | |
| dc.date.issued | 1991 | en_US |
| dc.identifier.uri | http://hdl.handle.net/10150/185347 | |
| dc.description.abstract | A point process model for the packet stream arising in teletraffic processes is the discrete, non-negative integer-valued, stationary process introduced by Neuts and Pearce. In this thesis, we examine an empirical approach to develop a monitoring scheme for that point process. Monitoring is a procedure of tracking a stochastic process to identify quickly the development of anomalous situations in the evolution of that process and detect their assignable causes. Further, a data-analytic scheme to evaluate the order of a Markov chain that quantifies the local dependence embedded in the point process and Walsh spectral techniques are examined. | |
| dc.language.iso | en | en_US |
| dc.publisher | The University of Arizona. | en_US |
| dc.rights | Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. | en_US |
| dc.subject | Dissertations, Academic | en_US |
| dc.subject | Operations research. | en_US |
| dc.title | Data-analytic and monitoring schemes for a class of discrete point processes. | en_US |
| dc.type | text | en_US |
| dc.type | Dissertation-Reproduction (electronic) | en_US |
| dc.identifier.oclc | 708653442 | en_US |
| thesis.degree.grantor | University of Arizona | en_US |
| thesis.degree.level | doctoral | en_US |
| dc.contributor.committeemember | Yakowitz, Sidney J. | en_US |
| dc.contributor.committeemember | Sanchez, Paul J. | en_US |
| dc.contributor.committeemember | Maier, Robert S. | en_US |
| dc.identifier.proquest | 9121537 | en_US |
| thesis.degree.discipline | Systems and Industrial Engineering | en_US |
| thesis.degree.discipline | Graduate College | en_US |
| thesis.degree.name | Ph.D. | en_US |
| refterms.dateFOA | 2018-06-23T04:53:25Z | |
| html.description.abstract | A point process model for the packet stream arising in teletraffic processes is the discrete, non-negative integer-valued, stationary process introduced by Neuts and Pearce. In this thesis, we examine an empirical approach to develop a monitoring scheme for that point process. Monitoring is a procedure of tracking a stochastic process to identify quickly the development of anomalous situations in the evolution of that process and detect their assignable causes. Further, a data-analytic scheme to evaluate the order of a Markov chain that quantifies the local dependence embedded in the point process and Walsh spectral techniques are examined. |
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