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The Savage Award

The Savage Award, named in honor of Leonard J. "Jimmie" Savage, is bestowed each year to two outstanding doctoral dissertations in Bayesian econometrics and statistics, one each in:
  • Theory and Methods:   for a dissertation that makes important original contributions to the foundations, theoretical developments, and/or general methodology of Bayesian analysis.
  • Applied Methodology:   for a dissertation that makes outstanding contributions with novel Bayesian analysis of a substantive problem that has potential to impact statistical practice in a field of application.
Each award is accompanied by a monetary prize.

The award was instituted by the NBER-NSF Seminar in Bayesian Inference in Econometrics and Statistics in 1977. ISBA and the ASA Section on Bayesian Statistical Science (SBSS) joined as co-sponsors in 1993.

Leonard J. "Jimmie" Savage

Born 20 November 1917, Jimmie Savage was graduated from the University of Michigan and later worked at the Institute for Advanced Study in Princeton, New Jersey, the University of Chicago, and the Statistical Research Group at Columbia University. Though his thesis advisor was Sumner Myers, he also credited Milton Friedman and W. Allen Wallis as his statistical mentors.

His most noted work was the 1954 book Foundations of Statistics, in which he put forward a theory of subjective and personal probability and statistics which forms one of the strands underlying Bayesian statistics and has applications to game theory.

One of Savage's indirect contributions was his discovery of the work of Louis Bachelier on stochastic models for asset prices and the mathematical theory of option pricing. Savage brought the work of Bachelier to the attention of Paul Samuelson. It was from Samuelson's subsequent writing that random walk (and subsequently Brownian motion) became fundamental to mathematical finance.

In 1951 he introduced the Minimax regret criterion used in decision theory. The Hewitt-Savage zero-one law is (in part) named after him.

Eligibility and Application Procedure

All dissertations that have not yet been nominated are eligible, but a dissertation may be nominated only once. A dissertation may be nominated by the author, by the advisor, the department chair, or by any ISBA or SBSS member (joining ISBA is easy). Nomination is made by on-line electronic submission of the dissertation and a letter that describes the main theoretical, methodological, and/or applied contributions of the thesis and specifies for which award the thesis is being nominated--- either Theory and Methods or Applied Methodology. Dissertations may be submitted on-line here.

For 2007, two $750 awards and two $250 honorable-mention prizes will be awarded, one each in the Theory and Methods and Applied Methodology categories. Submissions will open on 10 September 2007 and will close at midnight UTC on 10 October 2007. Nominated theses will be evaluated by the Savage Award Award Committee. Finalists will be selected by the end of January and will be invited to present their work at the ISBA 2008: 9th ISBA World Meeting in Hamilton Island, Australia in late July 2008, with the winners announced at the meeting.

Questions about the process may be sent to awards@bayesian.org.

Winners of the Savage Award

  • 2007
    Winners of the 2007 Savage Awards will be announced in July, 2008 at the Ninth Internationa ISBA World Meeting, ISBA '08. Finalist candidates are:
    • Theory & Methods
      Kostas Kalogeropoulos, Bayesian Inference for Multidimensional Diffusion Processes. Athens Univ Econ and Business; Petros Dellaportas, advisor
      Iain Murray, Advances in Markov chain Monte Carlo methods. Univ College London; Zoubin Ghahramani, advisor.
    • Applied Methodology
      Edoardo M. Airoldi, Bayesian Mixed-Membership Models of Complex and Evolving Networks. CMU; Stephen E. Fienberg and Kathleen Carley, advisors.
      Vladimir Minin, Exploring Evolutionary Heterogeneity with Change-Point Models, Gaussian Markov Random Fields, and Markov Chain Induced Counting Processes. UCLA; Marc Suchard, advisor.
  • 2006
    • Theory & Methods
      Surya Tokdar, Exploring Dirichlet Mixture and Logistic Gaussian Process Priors in Density Estimation, Regression and Sufficient Dimension Reduction. Purdue Univ; J.K. Ghosh, advisor.
      Pierpaolo de Blasi (Honorable Mention), Semiparametric models in Bayesian Event History Analysis. Bocconi U; Nils Lid Hjort & Pietro Muliere, advisors.
    • Applied Methodology
      Robert Gramacy, Bayesian Treed Gaussian Process Models. UCSC; Herbie Lee, advisor.
      Carlos Carvalho (Honorable Mention), Structure and Sparsity in High-Dimensional Multivariate Analysis. Duke Univ; Mike West, advisor.
  • 2005
    • Theory & Methods
      Xinyi Xu, Estimation of High Dimensional Predictive Densities. Univ Penn; Ed George, advisor.
      Taeryon Choi (Honorable Mention), Posterior Consistency in Nonparametric Regression Problems under Gaussian Process Priors. CMU; Mark Schervish, advisor.
    • Applied Methodology
      Dimitris Nicoloutsopoulos, Parametric and Bayesian Non-parametric Estimation of Copulas. UCL; Phil Dawid, advisor.
      Billy Amzal (Honorable Mention), Optimisation Bayésienne de Décisions et de Plans d'Expériences par Algorithmes Particulaires. Univ Paris Dauphine; Eric Parent & Christian Robert, advisors.
  • 2004
    • Theory and Methods
      Mario Trottini (Honorable Mention), Decision Models for Data Disclosure Limitation. CMU; Stephen Fienberg, advisor.
      Ramses Mena (Honorable Mention), Stationary Models using Latent Structures. Univ Bath; Stephen Walker, advisor.
    • Application Methodology
      Shane Jensen, Statistical Techniques for Examining Gene Regulation. Harvard Univ; Jun Liu, advisor.
      Jesus Palomo (Honorable Mention), Bayesian Methods in Bidding Processes. Rey Juan Carlos Univ; David Ríos Insua & Fabrizio Ruggeri, advisors.
  • 2003
    • Theory and Methods
      Chris Paciorek, Non-stationary Gaussian Processes for Regression and Spatial Modeling. CMU; Mark Schervish, advisor.
    • Application Methodology
      Louis T. Mariano, Information Accumulation, Model Selection, and Rater Behavior in Constructed Response Student Assessments. CMU; Brian Junker, advisor.
  • 2002
    • Theory and Methods
      Nicolas Chopin, Applications des Méthodes de Monte Carlo Séquentielles à la Statistique Bayésienne. Univ Paris Dauphine; Christian Robert, advisor.
    • Application Methodology
      Marc Suchard, Model Building and Selection in Bayesian Phylogenetic Reconstruction. UCLA; Robert Weiss, advisor.
  • 2001
    • Theory and Methods
      Luis E. Nieto-Barajas, Bayesian Nonparametric Survival Analysis via Markov Processes. Univ Bath; Stephen Walker, advisor.
    • Application Methodology
      J. R. Lockwood, Estimating Joint Distributions of Contaminants in U.S. Community Water System Sources. CMU; Mark Schervish, advisor.
  • 2000
    • Theory and Methods
      Peter Hoff (Co-winner), Constrained Nonparametric Estimation via Mixtures. Univ Wisconsin; Michael Newton, advisor.
      Tzee-Ming Huang (Co-winner), Convergence Rates for Posterior Distributions. CMU; Larry Wasserman, advisor.
    • Application Methodology
      Jeremy Oakley, Bayesian Uncertainty Analysis for Complex Computer Code. Univ Sheffield; Tony O'Hagan, advisor.
      Tim Hanson (Honorable Mention), Applied Bayesian Semiparametric Methods with Special Application to the Accelerated Failure Time Model and to Hierarchical Models for Screening UC Davis; Tim Hanson, advisor.
  • 1999
    • Theory and Methods
      Garrick L. Wallstrom, Consistency and Strong Inconsistency of Inferences. Univ Minnesota; Joe Eaton, advisor.
    • Application Methodology
      Clare E. Marshall, Statistical Methods for Institutional Comparisons. Cambridge Univ; David Spiegelhalter, advisor.
      Andrew S. Mugglin (Honorable Mention), Fully Model-Based Approaches for Spatially Misaligned Data. Univ Minnesota; Brad Carlin, advisor.
  • 1998
    Antonietta Mira, Ordering, splicing and splitting Monte Carlo Markov chains. Univ Minnesota; Luke Tierney, advisor.
    Jaelong Lee (Honorable Mention) Semiparametric Bayesian analysis: selection models and meteorolgical applications. Purdue Univ; Jim Berger, advisor.
  • 1997
    David Denison, Simulation Based Bayesian Non-parametric Regression Methods. Imperial College; Bani Mallick & Adrian Smith, advisors.
    Juha Heikkinen (Honorable Mention), Bayesian Smoothing and Step Functions in Nonparametric Estimation of Curves and Surfaces. Univ Helsinki; Antti Penttinen & Elija Arjas, advisors.
  • 1996
    Nariankadu D. Shyamalkumar, Contributions to Bayesian Nonparametrics and Bayesian Robustness. Purdue Univ; Jim Berger, advisor.
    Eric Bradlow (Honorable Mention), A Hierarchical Latent Response Model for Ordinal Data with "No Answer" Responses. Harvard Univ; Alan Zaslavsky, advisor.
    Max Chickering (Honorable Mention), Learning Bayesian Networks from Data. UCLA; David Heckerman, Richard Korf & Judea Pearl, advisors.
    Andrea Piesse (Honorable Mention), Coherent Predictive Probabilities. Univ Canterbury; John Deely & Frank Lad, advisors.
  • 1995
    Christopher K. Carter (Co-winner), On Markov Chain Monte Carlo for Linear State Space. Univ New South Wales; Robert Kohn, advisor.
    Alyson Wilson (Co-winner) Statistical Models for Shapes and Deformations. Duke Univ; Valen Johnson, advisor.
    Simon J. Godsill (Honorable Mention), The Restoration of Degraded Audio Signals.
    Ming-Hui Chen (Honorable Mention), Monte Carlo Markov Chain Sampling for Bayesian Computation with Applications. Purdue Univ; Jim Berger & Bruce Schmeiseer, advisors.
  • 1994
    Merlise A. Clyde, Bayesian Optimal Designs for Approximate Normality. Univ Minnesota; Kathryn Chaloner, advisor.
    Maria Del Carmen Fernandez Llana (Honorable Mention), Estudios Sobre Robustez Bayesiana Global. Univ Autonoma de Madrid.
    Paul Gustafson (Honorable Mention), Local Sensitivity of Posterior Expectations. CMU; Larry Wasserman, advisor.
    Debajyoti Sinha (Honorable Mention), Semiparametric Bayesian Analysis of Single and Multiple Time Event Data. Univ Rochester; W Jack Hall, advisor.
  • 1993
    Charles Jeremy York, Bayesian Methods for the Analysis of Misclassified or Incomplete Multivariate Data.
  • 1990
    Giovanni Parmigiani, Optimal Scheduling of Inspections with an Application to Medical Screening Tests.
  • 1989
    Valen E. Johnson, On Statistical Image Reconstruction.
  • 1988
    Michael David Escobar, Estimating the Means of Several Normal Populations by Nonparametric Estimation of the distribution of the Means.
  • 1987
    Peter E. Rossi, Specification and Analysis of Econometric Production Models.
  • 1986
    Mohan Delampady (Co-winner), Testing a Precise Hypothesis Interpreting P-Values from a Robust Bayesian Viewpoint.
    S. Sivaganesan (Co-winner), Robust Bayesian Analysis with Contamminated Classes.
    Herman K. van Dijk (Co-winner), Posterior Analysis of Econometric Models Using Monte Carlo Integration.
  • 1985
    Peter Jamison Lenk, Bayesian Nonparametric Predictive Distributions.
  • 1984
    Luc Bauwens (Co-winner), Bayesian Full Information Analysis of Simultaneous Equation Models.
    Peter C. Cranston (Co-winner), The Role of Time and Information in Bargaining.
  • 1983
    Paul H. Garthwaite, Assessment of Prior Distributions for Normal Linear Models.
  • 1982
    Soo Hong Chew, Two representation Theorems and Their Applications to Decision Theory.
  • 1981
    Robert Kass (Co-winner), The Riemannian Structure of Model Spaces: A Geometrical Approach to Inference.
    John P. O'Connor (Co-winner), A Certainty Equivlaent Based Metrization of Utility Function Space.
  • 1980
    Paul Milgram, The Structure of Information in Competitive Bidding.
  • 1979
    Kevin James McConway, The Combination of Experts' Opinions in Probability Assessment: Some Theoretical Considerations.
  • 1978
    Lorraine DeRobertis, The Use of Partial Prior Knowledge in Bayesian Inference.
    José Bernardo (Honorable Mention), The Use of Information in the Design and Analysis of Scientific Experimentation.
  • 1977
    Charles A. Holt, Bidding for Contracts.
    Robert Shore (Honorable Mention), A Bayesian Approach to the Spectral Analysis of Stationary Time Series.

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