Journal ArticleDOI
Large sample properties of simulations using latin hypercube sampling
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TLDR
In this paper, a method for producing Latin hypercube samples when the components of the input variables are statistically dependent is described, and the estimate is also shown to be asymptotically normal.Abstract:
Latin hypercube sampling (McKay, Conover, and Beckman 1979) is a method of sampling that can be used to produce input values for estimation of expectations of functions of output variables. The asymptotic variance of such an estimate is obtained. The estimate is also shown to be asymptotically normal. Asymptotically, the variance is less than that obtained using simple random sampling, with the degree of variance reduction depending on the degree of additivity in the function being integrated. A method for producing Latin hypercube samples when the components of the input variables are statistically dependent is also described. These techniques are applied to a simulation of the performance of a printer actuator.read more
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References
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Journal ArticleDOI
A comparison of three methods for selecting values of input variables in the analysis of output from a computer code
TL;DR: In this paper, two sampling plans are examined as alternatives to simple random sampling in Monte Carlo studies and they are shown to be improvements over simple sampling with respect to variance for a class of estimators which includes the sample mean and the empirical distribution function.
Journal ArticleDOI
A distribution-free approach to inducing rank correlation among input variables
TL;DR: In this article, a method for inducing a desired rank correlation matrix on a multivariate input random variable for use in a simulation study is introduced, which preserves the exact form of the marginal distributions on the input variables, and may be used with any type of sampling scheme for which correlation of input variables is a meaningful concept.
Risk methodology for geologic disposal of radioactive waste: small sample sensitivity analysis techniques for computer models, with an application to risk assessment
TL;DR: A generalization of Latin hypercube sampling is given that allows these areas of decision making in the face of uncertainty to be investigated without making additional computer runs.
Journal ArticleDOI
Small sample sensitivity analysis techniques for computer models.with an application to risk assessment
TL;DR: In this paper, Latin hypercube sampling has been shown to work well on this type of problem, and a judicious selection procedure for the choic of values of input variables is required, a variety of situations require that decisions and judgments be made in the face of uncertainty.
Journal ArticleDOI
A martingale approach to central limit theorems for exchangeable random variables
TL;DR: In this article, it was shown that by an appropriate choice of σ-fields, martingale methods can be used to obtain simple proofs of many of the central limit theorems known for triangular arrays of exchangeable random variables.