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Maximum entropy distributions with quantile information


  • Bajgiran, Amirsaman H.
  • Mardikoraem, Mahsa
  • Soofi, Ehsan S.


Quantiles are available in various problems for developing probability distributions. In some problems quantiles are elicited from experts and used for fitting parametric models, which induce non-elicited information. In some other problems comparisons are made with a quantile of an assumed model which is noncommittal to the quantile information. The maximum entropy (ME) principle provides models that avoid these issues. However, the information theory literature has been mainly concerned about models based on moment information. This paper explores the ME models that are the minimum elaborations of the uniform and moment-based ME models by quantiles. This property provides diagnostics for the utility of elaboration in terms of the information value of each type of information over the other. The ME model with quantiles and moments is represented as the mixture of truncated distributions on consecutive intervals whose shapes and existence are determined by the moments. Elaborations of several ME distributions by quantiles are presented. The ME model based only on quantiles elicited by the fixed interval method possesses a useful property for pooling information elicited from multiple experts. The elaboration of Laplace distribution is an extension of the information theory connection with minimum risk under symmetric loss functions to the asymmetric linear loss. This extension produces a new Asymmetric Laplace distribution. Application examples compare ME priors with a parametric model fitted to elicited quantiles, illustrate measuring uncertainty and disagreement of economic forecasters based on elicited probabilities, and adjust ME models for a fundamental quantile in an inventory management problem.

Suggested Citation

  • Bajgiran, Amirsaman H. & Mardikoraem, Mahsa & Soofi, Ehsan S., 2021. "Maximum entropy distributions with quantile information," European Journal of Operational Research, Elsevier, vol. 290(1), pages 196-209.
  • Handle: RePEc:eee:ejores:v:290:y:2021:i:1:p:196-209
    DOI: 10.1016/j.ejor.2020.07.052

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    References listed on IDEAS

    1. Engelberg, Joseph & Manski, Charles F. & Williams, Jared, 2009. "Comparing the Point Predictions and Subjective Probability Distributions of Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27, pages 30-41.
    2. Alwan, Layth C. & Ebrahimi, Nader & Soofi, Ehsan S., 1998. "Information theoretic framework for process control," European Journal of Operational Research, Elsevier, vol. 111(3), pages 526-542, December.
    3. Asadi, Majid & Ebrahimi, Nader & Soofi, Ehsan S., 2018. "Optimal hazard models based on partial information," European Journal of Operational Research, Elsevier, vol. 270(2), pages 723-733.
    4. Zellner, Arnold & Highfield, Richard A., 1988. "Calculation of maximum entropy distributions and approximation of marginalposterior distributions," Journal of Econometrics, Elsevier, vol. 37(2), pages 195-209, February.
    5. Andersson, Jonas & Jörnsten, Kurt & Nonås, Sigrid Lise & Sandal, Leif & Ubøe, Jan, 2013. "A maximum entropy approach to the newsvendor problem with partial information," European Journal of Operational Research, Elsevier, vol. 228(1), pages 190-200.
    6. Bera Anil K. & Galvao Antonio F. & Montes-Rojas Gabriel V. & Park Sung Y., 2016. "Asymmetric Laplace Regression: Maximum Likelihood, Maximum Entropy and Quantile Regression," Journal of Econometric Methods, De Gruyter, vol. 5(1), pages 79-101, January.
    7. Fleischhacker, Adam J. & Fok, Pak-Wing, 2015. "On the relationship between entropy, demand uncertainty, and expected loss," European Journal of Operational Research, Elsevier, vol. 245(2), pages 623-628.
    8. Poiraud-Casanova, Sandrine & Thomas-Agnan, Christine, 2000. "About monotone regression quantiles," Statistics & Probability Letters, Elsevier, vol. 48(1), pages 101-104, May.
    9. Georgia Perakis & Guillaume Roels, 2008. "Regret in the Newsvendor Model with Partial Information," Operations Research, INFORMS, vol. 56(1), pages 188-203, February.
    10. Majid Asadi & Nader Ebrahimi & Ehsan S. Soofi & Somayeh Zarezadeh, 2014. "New maximum entropy methods for modeling lifetime distributions," Naval Research Logistics (NRL), John Wiley & Sons, vol. 61(6), pages 427-434, September.
    11. Soroush Saghafian & Brian Tomlin, 2016. "The Newsvendor under Demand Ambiguity: Combining Data with Moment and Tail Information," Operations Research, INFORMS, vol. 64(1), pages 167-185, February.
    12. Ebrahimi, Nader & Soofi, Ehsan S. & Soyer, Refik, 2008. "Multivariate maximum entropy identification, transformation, and dependence," Journal of Multivariate Analysis, Elsevier, vol. 99(6), pages 1217-1231, July.
    13. Robert Rich & Joseph Tracy, 2010. "The Relationships among Expected Inflation, Disagreement, and Uncertainty: Evidence from Matched Point and Density Forecasts," The Review of Economics and Statistics, MIT Press, vol. 92(1), pages 200-207, February.
    14. Garthwaite, Paul H. & Kadane, Joseph B. & O'Hagan, Anthony, 2005. "Statistical Methods for Eliciting Probability Distributions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 680-701, June.
    15. Omid M. Ardakani & Nader Ebrahimi & Ehsan S. Soofi, 2018. "Ranking Forecasts by Stochastic Error Distance, Information and Reliability Measures," International Statistical Review, International Statistical Institute, vol. 86(3), pages 442-468, December.
    16. Plischke, Elmar & Borgonovo, Emanuele & Smith, Curtis L., 2013. "Global sensitivity measures from given data," European Journal of Operational Research, Elsevier, vol. 226(3), pages 536-550.
    17. Kajal Lahiri & Wuwei Wang, 2019. "Estimating macroeconomic uncertainty and discord using info-metrics," CESifo Working Paper Series 7674, CESifo.
    18. Mehdi Shoja & Ehsan S. Soofi, 2017. "Uncertainty, information, and disagreement of economic forecasters," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 796-817, October.
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    1. Majid Asadi & Karthik Devarajan & Nader Ebrahimi & Ehsan Soofi & Lauren Spirko‐Burns, 2022. "Elaboration Models with Symmetric Information Divergence," International Statistical Review, International Statistical Institute, vol. 90(3), pages 499-524, December.

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