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Vector computers, Monte Carlo simulation and regression analysis: An introduction

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  • Annink, B.
  • Kleijnen, J.P.C.

    (Tilburg University)

Abstract

Vector 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 Info

Paper provided by Tilburg University in its series Open Access publications from Tilburg University with number urn:nbn:nl:ui:12-369804.

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Date of creation: 1992
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Publication status: Published in Management Science (1992) v.38, p.170-181
Handle: RePEc:ner:tilbur:urn:nbn:nl:ui:12-369804

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Web page: http://www.tilburguniversity.edu/

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Cited by:
  1. 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.
  2. Bettonvil, B. & Kleijnen, J.P.C., 1994. "Identifying the important factors in simulation models with many factors," Discussion Paper 1994-114, Tilburg University, Center for Economic Research.

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