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Bayesian Estimation of Fuel Economy Potential Due to Technology Improvements

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Case Studies in Bayesian Statistics

Part of the book series: Lecture Notes in Statistics ((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.

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Author information

Authors and Affiliations

  1. University of Michigan, USA

    Richard W. Andrews

  2. Purdue University, USA

    James O. Berger

  3. University of Canterbury, New Zealand

    Murray H. Smith

Authors
  1. Richard W. Andrews
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  2. James O. Berger
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  3. Murray H. Smith
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Editor information

Editors and Affiliations

  1. Department of Health Care Policy, Harvard Medical School, 25 Shattuck St., Parcel B, 02115, Boston, MA, USA

    Constantine Gatsonis

  2. RAND, 1700 Main St, 90407, Santa Monica, CA, USA

    James S. Hodges

  3. Department of Statistics, Carnegie-Mellon University, 15213, Pittsburgh, PA, USA

    Robert E. Kass

  4. Department of Operations Research, The George Washington University, 20052, Washington, D.C., USA

    Nozer D. Singpurwalla

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© 1993 Springer-Verlag New York, Inc.

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Andrews, R.W., Berger, J.O., Smith, M.H. (1993). Bayesian Estimation of Fuel Economy Potential Due to Technology Improvements. In: Gatsonis, C., Hodges, J.S., Kass, R.E., Singpurwalla, N.D. (eds) Case Studies in Bayesian Statistics. Lecture Notes in Statistics, vol 83. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2714-4_1

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  • DOI: https://doi.org/10.1007/978-1-4612-2714-4_1

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-94043-4

  • Online ISBN: 978-1-4612-2714-4

  • eBook Packages: Springer Book Archive

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