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

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Author Info

  • 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)

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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File URL: http://dx.doi.org/10.1287/mnsc.38.2.170
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Bibliographic Info

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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Related research

Keywords: supercomputers; distribution sampling; multivariate distribution; common seeds; metamodel;

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Cited by:
  1. 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.
  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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