On the inefficiency of state-independent importance sampling in the presence of heavy tails

Achal Bassamboo; Sandeep Juneja; Assaf Zeevi

doi:10.1016/j.orl.2006.02.002

On the inefficiency of state-independent importance sampling in the presence of heavy tails

Achal Bassamboo^*, Sandeep Juneja, Assaf Zeevi

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

This paper proves that there does not exist an asymptotically optimal state-independent change-of-measure for estimating the probability that a random walk with heavy-tailed increments exceeds a "high" threshold before going below zero. Explicit bounds are given on the best asymptotic variance reduction that can be achieved by state-independent schemes.

Original language	English
Pages (from-to)	251-260
Number of pages	10
Journal	Operations Research Letters
Volume	35
Issue number	2
DOIs	https://doi.org/10.1016/j.orl.2006.02.002
State	Published - Mar 2007
Externally published	Yes

Keywords

Asymptotic analysis
Heavy tails
Importance sampling
State-dependent change-of-measure

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10.1016/j.orl.2006.02.002

Cite this

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title = "On the inefficiency of state-independent importance sampling in the presence of heavy tails",

abstract = "This paper proves that there does not exist an asymptotically optimal state-independent change-of-measure for estimating the probability that a random walk with heavy-tailed increments exceeds a {"}high{"} threshold before going below zero. Explicit bounds are given on the best asymptotic variance reduction that can be achieved by state-independent schemes.",

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AB - This paper proves that there does not exist an asymptotically optimal state-independent change-of-measure for estimating the probability that a random walk with heavy-tailed increments exceeds a "high" threshold before going below zero. Explicit bounds are given on the best asymptotic variance reduction that can be achieved by state-independent schemes.

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KW - Importance sampling

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On the inefficiency of state-independent importance sampling in the presence of heavy tails

Abstract

Keywords

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