Validation of Models: Statistical Techniques and Data Availability

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Abstract

This paper shows which statistical techniques can be used to validate simulation models, depending on which real-life data are available. Concerning this availability three situations are distinguished (i) no data, (ii) only output data, and (iii) both input and output data. In case (i) - no real data - the analysts can still experiment with the simulation model to obtain simulated data; such an experiment should be guided by the statistical theory on the design of experiments. In case (ii) - only output data - real and simulated output data can be compared through the well-known two-sample Student t statistic or certain other statistics. In case (iii) - input and output data - trace-driven simulation becomes possible, but validation should not proceed in the popular way (make a scatter plot with real and simulated outputs, fit a line, and test whether that line has unit slope and passes through the origin); alternative regression and bootstrap procedures are presented. Several case studies are summarized, to illustrate the three types of situations.
Original languageEnglish
Place of PublicationTilburg
PublisherOperations research
Number of pages9
Volume1999-104
Publication statusPublished - 1999

Publication series

NameCentER Discussion Paper
Volume1999-104

Keywords

  • Statistical methods
  • simulation models

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  • Cite this

    Kleijnen, J. P. C. (1999). Validation of Models: Statistical Techniques and Data Availability. (CentER Discussion Paper; Vol. 1999-104). Operations research.