Abstract
In this article, rare-event simulation for stochastic recurrence equations of the form [formula ommitted] is studied, where {An; n ≥ 1} and {Bn; n ≥ 1} are independent sequences consisting of independent and identically distributed real-valued random variables. It is assumed that the tail of the distribution of B1 is regularly varying, whereas the distribution of A1 has a suitably light tail. The problem of efficient estimation, via simulation, of quantities such as P{Xn> b} and P{supk=nXk> b} for large b and n is studied. Importance sampling strategies are investigated that provide unbiased estimators with bounded relative error as b and n tend to infinity.
Original language | English (US) |
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Pages (from-to) | 1-25 |
Number of pages | 25 |
Journal | ACM Transactions on Modeling and Computer Simulation |
Volume | 23 |
Issue number | 4 |
DOIs | |
State | Published - Oct 1 2013 |
Keywords
- Algorithms
- Importance sampling
- Performance
- Theory
- heavy-tails
- stochastic recurrence equations