Sequential methods for rare event simulation : theory and applications

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Abstract/Contents

Abstract
We consider rare events modeled as a Markov Chain hitting a certain rare set. A sequential importance sampling with resampling (SISR) method is introduced to provide a versatile approach for computing such probabilities of rare events. The method uses resampling to track the zero-variance importance measure associated with the event of interest. A general methodology for choosing the importance measure and resampling scheme to come up with an efficient estimator of the probability of occurrence of the rare event is developed and the distinction between light-tailed and heavy-tailed problems is highlighted. Applications include classic tail probabilities for sums of independent light-tailed or heavy-tailed random variables. Markovian extensions and simultaneous simulation are also given. The heuristics and the methodology can also be applied to more complex Monte Carlo problems that arise in recent works on the dynamic portfolio credit risk model.

Description

Type of resource text
Form electronic; electronic resource; remote
Extent 1 online resource.
Publication date 2010
Issuance monographic
Language English

Creators/Contributors

Associated with Deng, Shaojie
Associated with Stanford University, Department of Statistics
Primary advisor Lai, T. L
Thesis advisor Lai, T. L
Thesis advisor Giesecke, Kay
Thesis advisor Siegmund, David, 1941-
Advisor Giesecke, Kay
Advisor Siegmund, David, 1941-

Subjects

Genre Theses

Bibliographic information

Statement of responsibility Shaojie Deng.
Note Submitted to the Department of Statistics.
Thesis Thesis (Ph.D.)--Stanford University, 2010.
Location electronic resource

Access conditions

Copyright
© 2010 by Shaojie Deng
License
This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

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