The DOI matches its Crossref record.
Orthogonal Array-Based Latin Hypercubes
Abstract
In this article, we use orthogonal arrays (OA's) to construct Latin hypercubes. Besides preserving the univariate stratification properties of Latin hypercubes, these strength r OA-based Latin hypercubes also stratify each r-dimensional margin. Therefore, such OA-based Latin hypercubes provide more suitable designs for computer experiments and numerical integration than do general Latin hypercubes. We prove that when used for integration, the sampling scheme with OA-based Latin hypercubes offers a substantial improvement over Latin hypercube sampling.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems
Helton, J. C., & Davis, F. J. (2003) · Reliability Engineering & System Safety · doi:10.1016/s0951-8320(03)00058-9 · Cited by 2,688
Engineering Design via Surrogate Modelling: A Practical Guide
Forrester, A. I. J., Sóbester, A., & Keane, A. J. (2008) · doi:10.1002/9780470770801 · Cited by 2,309
A rigorous framework for optimization of expensive functions by surrogates
Booker, A. J., Dennis, J. E., Frank, P. D., Serafini, D. B., Torczon, V., & Trosset, M. W. (1999) · Structural Optimization · doi:10.1007/bf01197708 · Cited by 960
Computer Model Calibration Using High-Dimensional Output
Higdon, D., Gattiker, J., Williams, B., & Rightley, M. (2008) · Journal of the American Statistical Association · doi:10.1198/016214507000000888 · Cited by 867
Analysis of Support Vector Regression for Approximation of Complex Engineering Analyses
Clarke, S. M., Griebsch, J. H., & Simpson, T. W. (2005) · Journal of Mechanical Design · doi:10.1115/1.1897403 · Cited by 523
Metadata
- Authors
- Tang, B.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1993-12
- Volume
- 88
- Issue
- 424
- Pages
- 1392-1397
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W1967692477
- Cited by
- 858
- References
- 0
- FWCI
- 5.90
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Orthogonal Array-Based Latin Hypercubes. https://app.cochat.ai/papers/10.1080/01621459.1993.10476423
- The paper
- Tang, B. (1993). Orthogonal Array-Based Latin Hypercubes. Journal of the American Statistical Association. https://doi.org/10.1080/01621459.1993.10476423
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.