Library Subscription: Guest
International Journal for Uncertainty Quantification

Published 6 issues per year

ISSN Print: 2152-5080

ISSN Online: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

Indexed in

REFINED LATINIZED STRATIFIED SAMPLING: A ROBUST SEQUENTIAL SAMPLE SIZE EXTENSION METHODOLOGY FOR HIGH-DIMENSIONAL LATIN HYPERCUBE AND STRATIFIED DESIGNS

Volume 6, Issue 1, 2016, pp. 79-97
DOI: 10.1615/Int.J.UncertaintyQuantification.2016011333
Get accessGet access

ABSTRACT

A robust sequential sampling method, refined latinized stratified sampling, for simulation-based uncertainty quantification and reliability analysis is proposed. The method combines the benefits of the two leading approaches, hierarchical Latin hypercube sampling (HLHS) and refined stratified sampling, to produce a method that significantly reduces the variance of statistical estimators for very high-dimensional problems. The method works by hierarchically creating sample designs that are both Latin and stratified. The intermediate sample designs are then produced using the refined stratified sampling method. This causes statistical estimates to converge at rates that are equal to or better than HLHS while affording maximal flexibility in sample size extension (one-at-a-time or n-at-a-time sampling are possible) that does not exist in HLHS—which grows the sample size exponentially. The properties of the method are highlighted for several very high-dimensional problems, demonstrating the method has the distinct benefit of rapid convergence for transformations of all kinds.

CITED BY
  1. Wu Zeping, Wang Donghui, Okolo Patrick N., Zhao Kun, Zhang Weihua, Efficient space-filling and near-orthogonality sequential Latin hypercube for computer experiments, Computer Methods in Applied Mechanics and Engineering, 324, 2017. Crossref

  2. Shields Michael D., Adaptive Monte Carlo analysis for strongly nonlinear stochastic systems, Reliability Engineering & System Safety, 175, 2018. Crossref

  3. Xu Jun, Dang Chao, A novel fractional moments-based maximum entropy method for high-dimensional reliability analysis, Applied Mathematical Modelling, 75, 2019. Crossref

  4. Bhaduri Anindya, Gardner Jasmine, Abrams Cameron F., Graham-Brady Lori, Free energy calculation using space filled design and weighted reconstruction: a modified single sweep approach, Molecular Simulation, 46, 3, 2020. Crossref

  5. Bhaduri Anindya, Brandyberry David, Shields Michael D., Geubelle Philippe, Graham-Brady Lori, On the usefulness of gradient information in surrogate modeling: Application to uncertainty propagation in composite material models, Probabilistic Engineering Mechanics, 60, 2020. Crossref

  6. Novák Lukáš, Vořechovský Miroslav, Sadílek Václav, Shields Michael D., Variance-based adaptive sequential sampling for Polynomial Chaos Expansion, Computer Methods in Applied Mechanics and Engineering, 386, 2021. Crossref

  7. Xu Jun, Zhang Yu, Wang Ding, Dai Hongzhe, A Novel Structural Reliability Method on the Basis of Gaussian Mixture and Scaled Unscented Transformation, Journal of Engineering Mechanics, 147, 12, 2021. Crossref

  8. Taverniers Søren, Bosma Sebastian B. M., Tartakovsky Daniel M., Accelerated Multilevel Monte Carlo With Kernel‐Based Smoothing and Latinized Stratification, Water Resources Research, 56, 9, 2020. Crossref

  9. Pettersson Per, Krumscheid Sebastian , ADAPTIVE STRATIFIED SAMPLING FOR NONSMOOTH PROBLEMS , International Journal for Uncertainty Quantification, 12, 6, 2022. Crossref

Begell Digital Portal Begell Digital Library eBooks Journals References & Proceedings Research Collections Prices and Subscription Policies Begell House Contact Us Language English 中文 Русский Português German French Spain