RT Journal Article ID 36689f911d62e136 A1 Shields, Michael D. T1 REFINED LATINIZED STRATIFIED SAMPLING: A ROBUST SEQUENTIAL SAMPLE SIZE EXTENSION METHODOLOGY FOR HIGH-DIMENSIONAL LATIN HYPERCUBE AND STRATIFIED DESIGNS JF International Journal for Uncertainty Quantification JO IJUQ YR 2016 FD 2016-08-16 VO 6 IS 1 SP 79 OP 97 K1 uncertainty quantification K1 Monte Carlo simulation K1 stratified sampling K1 Latin hypercube sampling K1 sample size extension AB 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. PB Begell House LK https://www.dl.begellhouse.com/journals/52034eb04b657aea,6695b1fe0a01e538,36689f911d62e136.html