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Vector Computers, Monte Carlo Simulation and Regression Analysis: An Introduction

Listed author(s):
  • Jack P. C. Kleijnen

    (Department of Information Systems and Auditing, School of Business and Economics, Katholieke Universiteit Brabant (Tilburg University), 5000 LE Tilburg, The Netherlands)

  • Ben Annink

    (Department of Information Systems and Auditing, School of Business and Economics, Katholieke Universiteit Brabant (Tilburg University), 5000 LE Tilburg, The Netherlands)

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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Article provided by INFORMS in its journal Management Science.

Volume (Year): 38 (1992)
Issue (Month): 2 (February)
Pages: 170-181

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Handle: RePEc:inm:ormnsc:v:38:y:1992:i:2:p:170-181
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