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dc.contributor.authorRached, Nadhir B.
dc.contributor.authorBenkhelifa, Fatma
dc.contributor.authorKammoun, Abla
dc.contributor.authorAlouini, Mohamed-Slim
dc.contributor.authorTempone, Raul
dc.date.accessioned2017-01-02T08:42:39Z
dc.date.available2017-01-02T08:42:39Z
dc.date.issued2016-11-16
dc.identifier.citationBen Rached N, Benkhelifa F, Kammoun A, Alouini M-S, Tempone R (2016) On the generalization of the hazard rate twisting-based simulation approach. Statistics and Computing. Available: http://dx.doi.org/10.1007/s11222-016-9716-4.
dc.identifier.issn0960-3174
dc.identifier.issn1573-1375
dc.identifier.doi10.1007/s11222-016-9716-4
dc.identifier.urihttp://hdl.handle.net/10754/622227
dc.description.abstractEstimating the probability that a sum of random variables (RVs) exceeds a given threshold is a well-known challenging problem. A naive Monte Carlo simulation is the standard technique for the estimation of this type of probability. However, this approach is computationally expensive, especially when dealing with rare events. An alternative approach is represented by the use of variance reduction techniques, known for their efficiency in requiring less computations for achieving the same accuracy requirement. Most of these methods have thus far been proposed to deal with specific settings under which the RVs belong to particular classes of distributions. In this paper, we propose a generalization of the well-known hazard rate twisting Importance Sampling-based approach that presents the advantage of being logarithmic efficient for arbitrary sums of RVs. The wide scope of applicability of the proposed method is mainly due to our particular way of selecting the twisting parameter. It is worth observing that this interesting feature is rarely satisfied by variance reduction algorithms whose performances were only proven under some restrictive assumptions. It comes along with a good efficiency, illustrated by some selected simulation results comparing the performance of the proposed method with some existing techniques.
dc.publisherSpringer Nature
dc.relation.urlhttp://dx.doi.org/10.1007/s11222-016-9716-4
dc.subjectNaive Monte Carlo
dc.subjectRare events
dc.subjectImportance sampling
dc.subjectHazard rate twisting
dc.subjectLogarithmic efficient
dc.subjectTwisting parameter
dc.titleOn the generalization of the hazard rate twisting-based simulation approach
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentCenter for Uncertainty Quantification in Computational Science and Engineering (SRI-UQ)
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.identifier.journalStatistics and Computing
kaust.personRached, Nadhir B.
kaust.personBenkhelifa, Fatma
kaust.personKammoun, Abla
kaust.personAlouini, Mohamed-Slim
kaust.personTempone, Raul
dc.date.published-online2016-11-16
dc.date.published-print2018-01


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