Samenvatting
Many scientific disciplines use mathematical models to describe complicated real systems. Often, analytical methods are inadequate, so simulation is applied. This thesis focuses on computer intensive simulation experiments in Operations Research/Management Science. For such experiments it is necessary to apply interpolation. In this thesis, Kriging interpolation for random simulation is proposed and a novel type of Kriging  called Detrended Kriging  is developed. Kriging turns out to give better predictions in random simulation than classic loworder polynomial regression. Kriging is not sensitive to variance heterogeneity: i.e. Kriging is a robust method. Moreover, the thesis develops a novel method to select experimental designs for expensive simulation. This method is sequential, and accounts for the specific input/output function implied by the underlying simulation model. For deterministic simulation the designs are constructed through crossvalidation and jackknifing, whereas for random simulation the customization is achieved through bootstrapping. The novel method simulates relatively more input combinations in the interesting parts of the input/output function, and gives better predictions than traditional Latin Hypercube Sample designs with prefixed sample sizes.
Originele taal2  Engels 

Kwalificatie  Doctor in de Filosofie 
Toekennende instantie 

Begeleider(s)/adviseur 

Datum van toekenning  28 okt 2005 
Plaats van publicatie  Tilburg 
Uitgever  
Status  Gepubliceerd  2005 
Vingerafdruk Duik in de onderzoeksthema's van 'Kriging metamodeling for simulation'. Samen vormen ze een unieke vingerafdruk.
Citeer dit
Beers, van, W. C. M. (2005). Kriging metamodeling for simulation. Universiteit vanTilburg.