Erschienen in:CentER Discussion Paper Series ; No. 2014-076
Umfang:
1 Online-Ressource (22 p)
Sprache:
Englisch
DOI:
10.2139/ssrn.2293866
Identifikator:
Entstehung:
Anmerkungen:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments November 24, 2014 erstellt
Beschreibung:
Kriging is a popular method for estimating the global optimum of a simulated system. Kriging approximates the input/output function of the simulation model. Kriging also estimates the variances of the predictions of outputs for input combinations not yet simulated. These predictions and their variances are used by efficient global optimization"(EGO), to balance local and global search. This article focuses on two related questions: (1) How to select the next combination to be simulated when searching for the global optimum? (2) How to derive confidence intervals for outputs of input combinations not yet simulated? Classic Kriging simply plugs the estimated Kriging parameters into the formula for the predictor variance, so theoretically this variance is biased. This article concludes that practitioners may ignore this bias, because classic Kriging gives acceptable confidence intervals and estimates of the optimal input combination. This conclusion is based on bootstrapping and conditional simulation