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Bayesian estimation of manufacturing effects in a fuel economy model
dc.contributor.authorAndrews, R. W.
dc.contributor.authorBerger, J. O.
dc.contributor.authorSmith, M. H.
dc.date.accessioned2017-12-15T16:48:24Z
dc.date.available2017-12-15T16:48:24Z
dc.date.issued1993-12
dc.identifier.citationAndrews, R. W.; Berger, J. O.; Smith, M. H. (1993). "Bayesian estimation of manufacturing effects in a fuel economy model." Journal of Applied Econometrics 8(S1): S5-S18.
dc.identifier.issn0883-7252
dc.identifier.issn1099-1255
dc.identifier.urihttps://hdl.handle.net/2027.42/139996
dc.description.abstractThe analysis of fuel economy data results in estimates of the technology utilization by manufacturer and vehicle line. The analysis employs a hierarchical Bayesian regression model with random components representing vehicle lines and manufacturers. The model includes predictor variables which describe vehicle features, such as type of transmission, and vehicle line specific measurements, such as compression ratio. Non‐informative priors with novel modifications are used and the Bayes estimates are obtained by use of Gibbs sampling. The results show there is substantial variability among manufacturers in efficiently utilizing technology for fuel economy.
dc.publisherWiley Subscription Services, Inc., A Wiley Company
dc.titleBayesian estimation of manufacturing effects in a fuel economy model
dc.typeArticleen_US
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelStatistics and Numeric Data
dc.subject.hlbsecondlevelEconomics
dc.subject.hlbsecondlevelMathematics
dc.subject.hlbtoplevelBusiness and Economics
dc.subject.hlbtoplevelScience
dc.subject.hlbtoplevelSocial Sciences
dc.description.peerreviewedPeer Reviewed
dc.contributor.affiliationumSchool of Business Administration, University of Michigan, Ann Arbor, MI 48109–1234, USA
dc.contributor.affiliationotherStatistics Department, Purdue University, West Lafayette, IN 47907, USA
dc.contributor.affiliationotherMathematics Department, University of Canterbury, Christchurch, New Zealand
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/139996/1/3950080503_ftp.pdf
dc.identifier.doi10.1002/jae.3950080503
dc.identifier.sourceJournal of Applied Econometrics
dc.identifier.citedreferenceBerger, J. O. ( 1985 ), Bayesian Analysis and Statistical Decision Theory, 2nd edn, Springer‐Verlag, New York.
dc.identifier.citedreferenceTierney, L. ( 1991 ),‘ Markov chains for exploring posterior distribution ’,Technical Report No. 560, School of Statistics, University of Minnesota, Minneapolis.
dc.identifier.citedreferenceAndrews, R. W., J. O. Berger and M. H. Smith ( 1993 ),‘ Bayesian estimation of fuel economy potential due to technology improvements ’, in C. Gatsonis et al. (eds), Case Studies In Bayesian Statistics, Springer‐Verlag, New York.
dc.identifier.citedreferenceIMSL ( 1989 ), STAT/LIBRARY: FORTRAN Subroutines for Statistical Analysis, IMSL, Houston Texas.
dc.identifier.citedreferenceGeyer, C. ( 1991 ),‘ Markov chain Monte Carlo maximum likelihood. Technical Report ’, School of Statistics, University of Minnesota, Minneapolis.
dc.identifier.citedreferenceGeman, S. and D. Geman ( 1984 ),‘ Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images ’, IEEE Trans. Pattern Anal. Machine Intell., 6, 721 – 741.
dc.identifier.citedreferenceGelfand, A. E. and A. F. M. Smith ( 1990 ),‘ Sampling‐based approaches to calculating marginal densities ’, Journal of the American Statistical Association, 85, 398 – 409.
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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