Hierarchical refinement of Latin Hypercube Samples
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F14%3APU112316" target="_blank" >RIV/00216305:26110/14:PU112316 - isvavai.cz</a>
Alternative codes found
RIV/00216305:26110/15:PU115750
Result on the web
<a href="http://dx.doi.org/10.1111/mice.12088" target="_blank" >http://dx.doi.org/10.1111/mice.12088</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1111/mice.12088" target="_blank" >10.1111/mice.12088</a>
Alternative languages
Result language
angličtina
Original language name
Hierarchical refinement of Latin Hypercube Samples
Original language description
The objective In this article, a novel method for the extension of sample size in Latin Hypercube Sampling (LHS) is suggested. The method can be applied when an initial LH design is employed for the analysis of functions g of a random vector. The article explains how the statistical, sensitivity and reliability analyses of g can be divided into a hierarchical sequence of simulations with subsets of samples of a random vector in such a way that (i) the favorable properties of LHS are retained (the low number of simulations needed for statistically significant estimations of statistical parameters of function g with low estimation variability); (ii) the simulation process can be halted, for example, when the estimations reach a certain prescribed statistical significance. An important aspect of the method is that it efficiently simulates subsets of samples of random vectors while focusing on their correlation structure or any other objective function such as some measure of dependence, spatial distribution uniformity, discrepancy, etc. This is achieved by employing a robust algorithm based on combinatorial optimization of the mutual ordering of samples. The method is primarily intended to serve as a tool for computationally intensive evaluations of g where there is a need for pilot numerical studies, preliminary and subsequently refined estimations of statistical parameters, optimization of the progressive learning of neural networks, or during experimental design.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20101 - Civil engineering
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Computer-Aided Civil and Infrastructure Engineering
ISSN
1093-9687
e-ISSN
1467-8667
Volume of the periodical
2014
Issue of the periodical within the volume
00
Country of publishing house
US - UNITED STATES
Number of pages
18
Pages from-to
1-18
UT code for WoS article
000352790800006
EID of the result in the Scopus database
2-s2.0-84926526361