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New Perspectives on Statistical Decisions Under Ambiguity

Author

Listed:
  • Jörg Stoye

    (Department of Economics, Cornell University, Ithaca, New York 14853)

Abstract

This review summarizes and connects recent work on the foundations and applications of statistical decision theory. Minimax models of decisions making under ambiguity are identified as a thread running through several literatures. In axiomatic decision theory, these models motivated a large literature on modeling ambiguity aversion. Some findings of this literature are reported in a way that should be directly accessible to statisticians and econometricians. In statistical decision theory, the models inform a rich theory of estimation and treatment choice, which was recently extended to account for partial identification and thereby ambiguity that does not vanish with sample size. This literature is illustrated by discussing global, finite-sample admissible, and minimax decision rules for a number of stylized decision problems with point and partial identification.

Suggested Citation

  • Jörg Stoye, 2012. "New Perspectives on Statistical Decisions Under Ambiguity," Annual Review of Economics, Annual Reviews, vol. 4(1), pages 257-282, July.
    • Handle: RePEc:anr:reveco:v:4:y:2012:p:257-282
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    File URL: http://www.annualreviews.org/doi/abs/10.1146/annurev-economics-080511-110959
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    Citations

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    Cited by:

    1. T. D. Pol & S. Gabbert & H.-P. Weikard & E. C. Ierland & E. M. T. Hendrix, 2017. "A Minimax Regret Analysis of Flood Risk Management Strategies Under Climate Change Uncertainty and Emerging Information," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 68(4), pages 1087-1109, December.
    2. Tamini, Lota Dabio, 2012. "Optimal quality choice under uncertainty on market development," Working Papers 148589, Structure and Performance of Agriculture and Agri-products Industry (SPAA).
    3. Yuan Liao & Anna Simoni, 2012. "Semi-parametric Bayesian Partially Identified Models based on Support Function," Papers 1212.3267, arXiv.org, revised Nov 2013.
    4. repec:bos:wpaper:wp2013-001 is not listed on IDEAS
    5. Jörg Stoye, 2022. "Bounding infection prevalence by bounding selectivity and accuracy of tests: with application to early COVID-19 [False-negative results of initial RT-PCR assays for COVID-19: a systematic review]," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 1-14.
    6. Stoye, Jörg, 2015. "Choice theory when agents can randomize," Journal of Economic Theory, Elsevier, vol. 155(C), pages 131-151.
    7. Bruce A. Reinig & Ira Horowitz, 2018. "Using Mathematical Programming to Select and Seed Teams for the NCAA Tournament," Interfaces, INFORMS, vol. 48(3), pages 181-188, June.
    8. Tamini, Lota D., 2012. "Optimal quality choice under uncertainty on market development," MPRA Paper 40845, University Library of Munich, Germany.
    9. Isaiah Andrews & Jesse M. Shapiro, 2021. "A Model of Scientific Communication," Econometrica, Econometric Society, vol. 89(5), pages 2117-2142, September.
    10. Gabriel Carroll, 2015. "Robustness and Linear Contracts," American Economic Review, American Economic Association, vol. 105(2), pages 536-563, February.
    11. Karun Adusumilli & Friedrich Geiecke & Claudio Schilter, 2019. "Dynamically Optimal Treatment Allocation using Reinforcement Learning," Papers 1904.01047, arXiv.org, revised May 2022.

    More about this item

    Keywords

    statistical decision theory; minimax; minimax regret; treatment choice; partial identification;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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