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Model. Comput. Simul."],"published-print":{"date-parts":[[2014,2]]},"abstract":"<jats:p>Stochastic kriging has been studied as an effective metamodeling technique for approximating response surfaces in the context of stochastic simulation. In a simulation experiment, an analyst typically needs to estimate relevant metamodel parameters and further do prediction; therefore, the impact of parameter estimation on the performance of the metamodel-based predictor has drawn some attention in the literature. However, how the standard stochastic kriging predictor is affected by the presence of bias in finite-sample estimates has not yet been fully investigated. In this article, we study the predictive performance and investigate optimal budget allocation rules subject to a fixed computational budget constraint. Furthermore, we extend the analysis to two-level or nested simulation, which has been recently documented in the risk management literature, with biased estimators.<\/jats:p>","DOI":"10.1145\/2567893","type":"journal-article","created":{"date-parts":[[2014,3,18]],"date-time":"2014-03-18T12:09:07Z","timestamp":1395144547000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":30,"title":["Stochastic kriging with biased sample estimates"],"prefix":"10.1145","volume":"24","author":[{"given":"Xi","family":"Chen","sequence":"first","affiliation":[{"name":"Virginia Commonwealth University, Richmond, VA"}]},{"given":"Kyoung-Kuk","family":"Kim","sequence":"additional","affiliation":[{"name":"Korea Advanced Institute of Science and Technology, Daejeon, South Korea"}]}],"member":"320","published-online":{"date-parts":[[2014,2]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1287\/opre.1090.0754"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.1110.1330"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2133390.2133391"},{"key":"e_1_2_2_4_1","volume-title":"Proceedings of the 2012 Winter Simulation Conference, C. 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