Vector computers, Monte Carlo simulation and regression analysis: An introduction
AbstractVector computers provide a new tool for management scientists. The application of that tool requires thinking in vector mode. This mode is examined in the context of Monte Carlo experiments with regression models; these regression models may serve as metamodels in simulation experiments. The vector mode needs to exploit a specific dimension of the Monte Carlo experiment, namely the replicates of that experiment. Taking advantage of the machine architecture gives a code that computes Ordinary Least Squares estimates on a Cyber 205 in only 2% of the time needed on a Vax 8700. For Generalized Least Squares estimates, however, the code runs slower on the Cyber 205 than on the VAX, if the regression model is small; for large models the CYBER 205 runs much faster.
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Bibliographic InfoPaper provided by Tilburg University in its series Open Access publications from Tilburg University with number urn:nbn:nl:ui:12-369804.
Date of creation: 1992
Date of revision:
Publication status: Published in Management Science (1992) v.38, p.170-181
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Web page: http://www.tilburguniversity.edu/
Other versions of this item:
- Jack P. C. Kleijnen & Ben Annink, 1992. "Vector Computers, Monte Carlo Simulation and Regression Analysis: An Introduction," Management Science, INFORMS, vol. 38(2), pages 170-181, February.
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- Bettonvil, Bert & Kleijnen, Jack P. C., 1997.
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Elsevier, vol. 96(1), pages 180-194, January.
- Kleijnen, J.P.C. & Bettonvil, B.W.M., 1997. "Searching for important factors in simulation models with many factors: Sequential bifurcation," Open Access publications from Tilburg University urn:nbn:nl:ui:12-73905, Tilburg University.
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