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• W2097268682 abstract "Large deviations theory is a well-studied area which has shown to have numerous applications. The typical results, however, assume that the underlying random variables are either i.i.d. or exhibit some form of Markovian dependence. Our interest in this paper is to study the validity of large deviations results in the context of estimators built with Latin Hypercube sampling, a well-known sampling technique for variance reduction. We show that a large deviation principle holds for Latin Hypercube sampling for functions in one dimension and for separable multi-dimensional functions. Moreover, the upper bound of the probability of a large deviation in these cases is no higher under Latin Hypercube sampling than it is under Monte Carlo sampling. We extend the latter property to functions that preserve negative dependence (such as functions that are monotone in each argument). Numerical experiments illustrate the theoretical results presented in the paper." @default.
• W2097268682 created "2016-06-24" @default.
• W2097268682 creator A5016276353 @default.
• W2097268682 creator A5066781794 @default.
• W2097268682 date "2005-12-04" @default.
• W2097268682 modified "2023-09-26" @default.
• W2097268682 title "Some large deviations results for Latin hypercube sampling" @default.
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• W2097268682 doi "https://doi.org/10.5555/1162708.1162824" @default.
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