Paper Info
Title
A New Metric for Measuring Metamodels Quality-of-Fit for Deterministic Simulations
Abstract
Metamodels are used to provide simpler prediction means than the complex simulation models they approximate. Accuracy of a metamodel is one fundamental criterion that is used as the basis for accepting or rejecting a metamodel. Average-based metrics such as root-mean-square error RMSE and R-square are often used. Like all other average-based statistics, these measures are sensitive to sample sizes unless the number of test points in these samples is adequate. A new metric that can be used to measure metamodels fit quality, called metamodel acceptability score MAS, is introduced. The proposed metric gives readily interpretable meaning to metamodels acceptability. Furthermore, initial studies show that MAS is less sensitive to test sample sizes compared to average-based validation measures
Year
DOI
Venue
2006
10.1109/WSC.2006.323171
Winter Simulation Conference
Keywords
Field
DocType
average-based statistic,test point,metamodel accuracy,new metric,deterministic simulations,statistical analysis,deterministic simulation,proposed metric,quality-of-fit metamodels,metamodels fit quality,metamodels quality-of-fit,average-based metrics,metamodel acceptability score,sample size,digital simulation,metamodel measurement,metamodels acceptability,mean square error methods,average-based validation measure,meta model,root mean square error,simulation model
Data mining,Computer science,Deterministic simulation,Mean squared error,Simulation modeling,Metamodeling,Sample size determination,Statistical analysis
Conference
ISBN
Citations 
PageRank 
1-4244-0501-7
4
0.63
References 
Authors
4
1
Name
Order
Citations
PageRank
Husam Hamad1122.81