The Annals of Statistics

On Latin hypercube sampling

Wei-Liem Loh

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Abstract

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 $\hat{\mu}_n$ based on a Latin hypercube sample. The second establishes sufficient conditions on the convergence rate in the strong law for $\hat{\mu}_n$. 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.

Article information

Source
Ann. Statist., Volume 24, Number 5 (1996), 2058-2080.

Dates
First available in Project Euclid: 20 November 2003

Permanent link to this document
https://projecteuclid.org/euclid.aos/1069362310

Digital Object Identifier
doi:10.1214/aos/1069362310

Mathematical Reviews number (MathSciNet)
MR1421161

Zentralblatt MATH identifier
0867.62005

Subjects
Primary: 62D05: Sampling theory, sample surveys
Secondary: 62E20: Asymptotic distribution theory 62G15: Tolerance and confidence regions

Keywords
Berry-Esseen bound confidence regions Latin hypercube sampling multivariate central limit theorem Stein's method strong law of large numbers

Citation

Loh, Wei-Liem. On Latin hypercube sampling. Ann. Statist. 24 (1996), no. 5, 2058--2080. doi:10.1214/aos/1069362310. https://projecteuclid.org/euclid.aos/1069362310


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