Some large deviations results for Latin Hypercube sampling

Shane S. Drew, Tito Homem-de-Mello

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

11 Citas (Scopus)


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.

Idioma originalInglés
Título de la publicación alojadaProceedings of the 2005 Winter Simulation Conference
Número de páginas9
EstadoPublicada - 2005
Publicado de forma externa
Evento2005 Winter Simulation Conference - Orlando, FL, Estados Unidos
Duración: 4 dic. 20057 dic. 2005

Serie de la publicación

NombreProceedings - Winter Simulation Conference
ISSN (versión impresa)0891-7736


Conferencia2005 Winter Simulation Conference
País/TerritorioEstados Unidos
CiudadOrlando, FL


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