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Bayesian Estimation of Fuel Economy Potential Due to Technology Improvements

  • Richard W. Andrews
  • James O. Berger
  • Murray H. Smith
Part of the Lecture Notes in Statistics book series (LNS, volume 83)

Abstract

There is currently considerable Congressional activity seeking to mandate drastic increases in fuel efficiency of automobiles. A key question is — How much of an increase in fuel economy is possible through implementation of existing technology?

This question is studied using an EPA data base of over 3000 vehicles. The data analysis was done using a hierarchical Bayesian (random effects) model, with computations being performed via Gibbs sampling. Interesting features of this analysis included the use of a “shrinkage prior” on the fixed effects (regression coefficients of fuel economy), and use of parameter constraints.

For prediction of technology effects it was also necessary to obtain subjective assessments from engineers, not of fuel economy — but of how addition of technologies requires adjustment of other characteristics of automobiles so as to keep “performance” constant. A Delphi assessment scheme was used, with the results being modeled by a multivariate split-normal distribution. The combination of this information with that from the data analysis involved some interesting questions in decision-theoretic prediction.

Keywords

Gibbs Sampler Auxiliary Variable Fuel Economy Bayesian Estimation Torque Converter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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Copyright information

© Springer-Verlag New York, Inc. 1993

Authors and Affiliations

  • Richard W. Andrews
    • 1
  • James O. Berger
    • 2
  • Murray H. Smith
    • 3
  1. 1.University of MichiganUSA
  2. 2.Purdue UniversityUSA
  3. 3.University of CanterburyNew Zealand

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