Heuristics for the regression of stochastic simulations

A J Turner, S Balestrini-Robinson and D Mavris

Journal of Simulation, 2013, vol. 7, issue 4, 229-239

Abstract: Modelling and simulation environments that are stochastic in nature present a multitude of problems in the creation of meta-models, notably the reduction of the quality of fit due to statistical error. This report addresses this issue by first determining the minimum number of repetitions required for interval estimates to hold true regardless of interval width. These intervals are then used for meta-model regressions. Four measures were examined. Sample mean and variance interval coverage accuracy was studied by varying skewness, kurtosis, desired coverage, and sample size within the Pearson family of distributions. Binomial proportion and quantile interval coverage accuracy was studied with the standard normal with varying sample size, desired coverage, and quantile level. Finally, heuristic measures to determine how repetitions are related to the quality of the meta-model fit were developed based on the experimentation with a canonical problem. The ratio of confidence interval width to the range of sample measures was found to be an indicator of the impact of statistical error on the quality of model fit. Regression methods of weighted least squares (WLS), ordinary least squares for constant sample sizes, and constant interval widths were compared. The WLS method is suggested for stochastic regressions of simulations.

Date: 2013
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1057/jos.2013.1 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tjsmxx:v:7:y:2013:i:4:p:229-239

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjsm20

DOI: 10.1057/jos.2013.1

Access Statistics for this article

Journal of Simulation is currently edited by Christine Currie

More articles in Journal of Simulation from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().