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Generalized Chebychev Inequalities: Theory and Applications in Decision Analysis

Author

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  • James E. Smith

    (Duke University, Durham, North Carolina)

Abstract

In many decision analysis problems, we have only limited information about the relevant probability distributions. In problems like these, it is natural to ask what conclusions can be drawn on the basis of this limited information. For example, in the early stages of analysis of a complex problem, we may have only limited fractile information for the distributions in the problem; what can we say about the optimal strategy or certainty equivalents given these few fractiles? This paper describes a very general framework for analyzing these kinds of problems where, given certain “moments” of a distribution, we can compute bounds on the expected value of an arbitrary “objective” function. By suitable choice of moment and objective functions we can formulate and solve many practical decision analysis problems. We describe the general framework and theoretical results, discuss computational strategies, and provide specific results for examples in dynamic programming, decision analysis with incomplete information, Bayesian statistics, and option pricing.

Suggested Citation

  • James E. Smith, 1995. "Generalized Chebychev Inequalities: Theory and Applications in Decision Analysis," Operations Research, INFORMS, vol. 43(5), pages 807-825, October.
    • Handle: RePEc:inm:oropre:v:43:y:1995:i:5:p:807-825
    DOI: 10.1287/opre.43.5.807
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    8. Villegas, Andrés M. & Medaglia, Andrés L. & Zuluaga, Luis F., 2012. "Computing bounds on the expected payoff of Alternative Risk Transfer products," Insurance: Mathematics and Economics, Elsevier, vol. 51(2), pages 271-281.
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    10. Zuluaga, Luis F. & Peña, Javier & Du, Donglei, 2009. "Third-order extensions of Lo's semiparametric bound for European call options," European Journal of Operational Research, Elsevier, vol. 198(2), pages 557-570, October.
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    15. J. A. Primbs, 2010. "SDP Relaxation of Arbitrage Pricing Bounds Based on Option Prices and Moments," Journal of Optimization Theory and Applications, Springer, vol. 144(1), pages 137-155, January.
    16. Derek Singh & Shuzhong Zhang, 2020. "Tight Bounds for a Class of Data-Driven Distributionally Robust Risk Measures," Papers 2010.05398, arXiv.org, revised Oct 2020.
    17. Li, Xiaobo & Natarajan, Karthik & Teo, Chung-Piaw & Zheng, Zhichao, 2014. "Distributionally robust mixed integer linear programs: Persistency models with applications," European Journal of Operational Research, Elsevier, vol. 233(3), pages 459-473.
    18. Viet Anh Nguyen & Fan Zhang & Shanshan Wang & Jose Blanchet & Erick Delage & Yinyu Ye, 2021. "Robustifying Conditional Portfolio Decisions via Optimal Transport," Papers 2103.16451, arXiv.org, revised Apr 2024.
    19. J. Eric Bickel & James E. Smith, 2006. "Optimal Sequential Exploration: A Binary Learning Model," Decision Analysis, INFORMS, vol. 3(1), pages 16-32, March.
    20. van Eekelen, Wouter, 2023. "Distributionally robust views on queues and related stochastic models," Other publications TiSEM 9b99fc05-9d68-48eb-ae8c-9, Tilburg University, School of Economics and Management.
    21. Nikolaus Schweizer & Nora Szech, 2018. "Optimal Revelation of Life-Changing Information," Management Science, INFORMS, vol. 64(11), pages 5250-5262, November.
    22. Ioana Popescu, 2005. "A Semidefinite Programming Approach to Optimal-Moment Bounds for Convex Classes of Distributions," Mathematics of Operations Research, INFORMS, vol. 30(3), pages 632-657, August.
    23. Bernhard Kasberger, 2022. "An Equilibrium Model of the First-Price Auction with Strategic Uncertainty: Theory and Empirics," Papers 2202.07517, arXiv.org, revised Mar 2022.
    24. Ioana Popescu, 2007. "Robust Mean-Covariance Solutions for Stochastic Optimization," Operations Research, INFORMS, vol. 55(1), pages 98-112, February.
    25. Henry Lam & Clementine Mottet, 2017. "Tail Analysis Without Parametric Models: A Worst-Case Perspective," Operations Research, INFORMS, vol. 65(6), pages 1696-1711, December.

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