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Abstract
En 中文
This paper contains a collection of results on Latin hypercube sampling. The first result is a Berry-Esseen-type bound for the multivariate central limit theorem of the sample mean <(mu)over cap>(n) based on a Latin hypercube sample. The second establishes sufficient conditions on the convergence rate in the strong law for <(mu)over cap>(n). Finally motivated by the concept of empirical likelihood, a way of constructing nonparametric confidence regions based on Latin hypercube samples is proposed for vector means.
Keywords:
Berry-Esseen bound
confidence regions
Latin hypercube sampling
multivariate central limit theorem
Stein's method
strong law of large numbers
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