Efficient prediction designs for random fields
Identifieur interne : 000328 ( France/Analysis ); précédent : 000327; suivant : 000329Efficient prediction designs for random fields
Auteurs : Werner G. Müller [Autriche] ; Luc Pronzato [France] ; Joao Rendas [France] ; Helmut Waldl [Autriche]Source :
- Applied Stochastic Models in Business and Industry [ 1524-1904 ] ; 2014.
Abstract
For estimation and predictions of random fields, it is increasingly acknowledged that the kriging variance may be a poor representative of true uncertainty. Experimental designs based on more elaborate criteria that are appropriate for empirical kriging (EK) are then often non-space-filling and very costly to determine. In this paper, we investigate the possibility of using a compound criterion inspired by an equivalence theorem type relation to build designs quasi-optimal for the EK variance when space-filling designs become unsuitable. Two algorithms are proposed, one relying on stochastic optimization to explicitly identify the Pareto front, whereas the second uses the surrogate criteria as local heuristic to choose the points at which the (costly) true EK variance is effectively computed. We illustrate the performance of the algorithms presented on both a simple simulated example and a real oceanographic dataset. © 2014 The Authors.
Url:
DOI: 10.1002/asmb.2084
PubMed: 26300698
PubMed Central: 4540167
Affiliations:
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PMC:4540167Le document en format XML
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Müller</name><affiliation wicri:level="1"><nlm:aff id="au1"><institution>Department of Applied Statistics, Johannes-Kepler-University of Linz</institution><addr-line>Linz, Austria</addr-line></nlm:aff><country xml:lang="fr">Autriche</country><wicri:regionArea>Linz</wicri:regionArea></affiliation></author><author><name sortKey="Pronzato, Luc" sort="Pronzato, Luc" uniqKey="Pronzato L" first="Luc" last="Pronzato">Luc Pronzato</name><affiliation wicri:level="1"><nlm:aff id="au2"><institution>Laboratoire I3S, CNRS/Université de Nice-Sophia Antipolis</institution><addr-line>Nice, France</addr-line></nlm:aff><country xml:lang="fr">France</country><wicri:regionArea>Nice</wicri:regionArea></affiliation></author><author><name sortKey="Rendas, Joao" sort="Rendas, Joao" uniqKey="Rendas J" first="Joao" last="Rendas">Joao Rendas</name><affiliation wicri:level="1"><nlm:aff id="au2"><institution>Laboratoire I3S, CNRS/Université de Nice-Sophia Antipolis</institution><addr-line>Nice, France</addr-line></nlm:aff><country xml:lang="fr">France</country><wicri:regionArea>Nice</wicri:regionArea></affiliation></author><author><name sortKey="Waldl, Helmut" sort="Waldl, Helmut" uniqKey="Waldl H" first="Helmut" last="Waldl">Helmut Waldl</name><affiliation wicri:level="1"><nlm:aff id="au1"><institution>Department of Applied Statistics, Johannes-Kepler-University of Linz</institution><addr-line>Linz, Austria</addr-line></nlm:aff><country xml:lang="fr">Autriche</country><wicri:regionArea>Linz</wicri:regionArea></affiliation></author></analytic><series><title level="j">Applied Stochastic Models in Business and Industry</title><idno type="ISSN">1524-1904</idno><idno type="eISSN">1526-4025</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>For estimation and predictions of random fields, it is increasingly acknowledged that the kriging variance may be a poor representative of true uncertainty. Experimental designs based on more elaborate criteria that are appropriate for empirical kriging (EK) are then often non-space-filling and very costly to determine. In this paper, we investigate the possibility of using a compound criterion inspired by an equivalence theorem type relation to build designs quasi-optimal for the EK variance when space-filling designs become unsuitable. Two algorithms are proposed, one relying on stochastic optimization to explicitly identify the Pareto front, whereas the second uses the surrogate criteria as local heuristic to choose the points at which the (costly) true EK variance is effectively computed. We illustrate the performance of the algorithms presented on both a simple simulated example and a real oceanographic dataset. © 2014 The Authors. <italic>Applied Stochastic Models in Business and Industry</italic> published by John Wiley & Sons, Ltd.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Cressie, N" uniqKey="Cressie N">N Cressie</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Fang, Kt" uniqKey="Fang K">KT Fang</name></author><author><name sortKey="Li, R" uniqKey="Li R">R Li</name></author><author><name sortKey="Sudjianto, A" uniqKey="Sudjianto A">A Sudjianto</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kleijnen, Jpc" uniqKey="Kleijnen J">JPC Kleijnen</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Rasmussen, Ce" uniqKey="Rasmussen C">CE Rasmussen</name></author><author><name sortKey="Williams, Cki" 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Stegun</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Weisstein, Ew" uniqKey="Weisstein E">EW Weisstein</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>Autriche</li><li>France</li></country></list><tree><country name="Autriche"><noRegion><name sortKey="Muller, Werner G" sort="Muller, Werner G" uniqKey="Muller W" first="Werner G" last="Müller">Werner G. Müller</name></noRegion><name sortKey="Waldl, Helmut" sort="Waldl, Helmut" uniqKey="Waldl H" first="Helmut" last="Waldl">Helmut Waldl</name></country><country name="France"><noRegion><name sortKey="Pronzato, Luc" sort="Pronzato, Luc" uniqKey="Pronzato L" first="Luc" last="Pronzato">Luc Pronzato</name></noRegion><name sortKey="Rendas, Joao" sort="Rendas, Joao" uniqKey="Rendas J" first="Joao" last="Rendas">Joao Rendas</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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