Optimizing the design of a latin hypercube sampling estimator

Alexander J. Zolan, John J. Hasenbein, David P. Morton

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Stratified sampling and Latin hypercube sampling (LHS) reduce variance, relative to naïve Monte Carlo sampling, by partitioning the support of a random vector into strata. When creating these estimators, we must determine: (i) the number of strata; and, (ii) the partition that defines the strata. In this paper, we address the second point by formulating a nonlinear optimization model that designs the strata to yield a minimum-variance stratified sampling estimator. Under a discrete set of candidate boundary points, the optimization model can be solved via dynamic programming. We extend this technique to LHS, using an approximation of estimator variance to obtain strata for the domain of a multivariate function. Empirical results show significant variance reduction compared to using equal-probability strata for LHS or naïve Monte Carlo sampling.

Original languageEnglish (US)
Title of host publication2017 Winter Simulation Conference, WSC 2017
EditorsVictor Chan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages12
ISBN (Electronic)9781538634288
StatePublished - Jun 28 2017
Event2017 Winter Simulation Conference, WSC 2017 - Las Vegas, United States
Duration: Dec 3 2017Dec 6 2017

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736


Other2017 Winter Simulation Conference, WSC 2017
Country/TerritoryUnited States
CityLas Vegas

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications


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