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Experimental designs based on more elaborate criteria that are appropriate for empirical kriging (EK) are then often non\u2010space\u2010filling 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\u2010optimal for the EK variance when space\u2010filling 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. \u00a9 2014 The Authors. <jats:italic>Applied Stochastic Models in Business and Industry<\/jats:italic> published by John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/asmb.2084","type":"journal-article","created":{"date-parts":[[2014,11,26]],"date-time":"2014-11-26T12:13:23Z","timestamp":1417004003000},"page":"178-194","source":"Crossref","is-referenced-by-count":8,"title":["Efficient prediction designs for random fields"],"prefix":"10.1002","volume":"31","author":[{"given":"Werner G.","family":"M\u00fcller","sequence":"first","affiliation":[{"name":"Department of Applied Statistics Johannes\u2010Kepler\u2010University of Linz  Linz Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luc","family":"Pronzato","sequence":"additional","affiliation":[{"name":"Laboratoire I3S CNRS\/Universit\u00e9 de Nice\u2010Sophia Antipolis  Nice 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