Confidence intervals for quantiles when applying replicated latin hypercube sampling and sectioning

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

1 Scopus citations

Abstract

Latin hypercube sampling (LHS), which can be viewed as an extension of stratified sampling to multiple dimensions, is a variance-reduction technique that increases statistical efficiency by inducing correlation among the outputs. We use a method known as sectioning to develop confidence intervals for quantiles when applying replicated Latin hypercube sampling (rLHS). Both two-sided and one-sided confidence intervals are given, and the intervals are asymptotically valid. One application of the technique is for uncertainty and safety analyses of nuclear power plants.

Original languageEnglish (US)
Title of host publicationEnergy, Climate and Environment Modeling and Simulation 2012, ECEMS 2012 - 2012 Autumn Simulation Multiconference, AutumnSim 2012
Pages12-19
Number of pages8
Edition16
StatePublished - Dec 1 2012
EventEnergy, Climate and Environment Modeling and Simulation 2012, ECEMS 2012, Part of the 2012 Autumn Simulation Multiconference, AutumnSim 2012 - San Diego, CA, United States
Duration: Oct 28 2012Oct 31 2012

Publication series

NameSimulation Series
Number16
Volume44
ISSN (Print)0735-9276

Other

OtherEnergy, Climate and Environment Modeling and Simulation 2012, ECEMS 2012, Part of the 2012 Autumn Simulation Multiconference, AutumnSim 2012
CountryUnited States
CitySan Diego, CA
Period10/28/1210/31/12

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

Keywords

  • Confidence interval
  • Latin hypercube sampling
  • Quantile
  • Sectioning

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