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 InfoArticle provided by INFORMS in its journal Management Science.
Volume (Year): 38 (1992)
Issue (Month): 2 (February)
supercomputers; distribution sampling; multivariate distribution; common seeds; metamodel;
Other versions of this item:
- Annink, B. & Kleijnen, J.P.C., 1992. "Vector computers, Monte Carlo simulation and regression analysis: An introduction," Open Access publications from Tilburg University urn:nbn:nl:ui:12-369804, Tilburg University.
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- 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.
- Bettonvil, Bert & Kleijnen, Jack P. C., 1997. "Searching for important factors in simulation models with many factors: Sequential bifurcation," European Journal of Operational Research, Elsevier, vol. 96(1), pages 180-194, January.
- Bettonvil, B. & Kleijnen, J.P.C., 1994.
"Identifying the important factors in simulation models with many factors,"
1994-114, Tilburg University, Center for Economic Research.
- Bettonvil, B.W.M. & Kleijnen, J.P.C., 1991. "Identifying the important factors in simulation models with many factors," Research Memorandum 498, Tilburg University, Faculty of Economics and Business Administration.
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