Using Experts’ Noisy Quantile Judgments to Quantify Risks: Theory and Application to Agribusiness

Published Online:https://doi.org/10.1287/opre.2017.1627

References

  • Akcay A, Biller B, Tayur S (2011) Improved inventory targets in the presence of limited historical demand data. Manufacturing Service Oper. Management 13(3):297–309.LinkGoogle Scholar
  • Ayvaci MUS, Ahsen ME, Raghunathan S, Gharibi Z (2017) Timing the use of breast cancer risk information in biopsy decision making. Production Oper. Management. Forthcoming.CrossrefGoogle Scholar
  • Baker E, Solak S (2014) Management of energy technology for sustainability: How to fund energy technology research and development. Production Oper. Management 23(3):348–365.CrossrefGoogle Scholar
  • Bansal S, Nagarajan M (2017) Product portfolio management with production flexibility in agribusiness. Oper. Res. 65(4):914–930.LinkGoogle Scholar
  • Bassett G Jr, Koenker R (1978) Asymptotic theory of least absolute error regression. J. Amer. Statist. Assoc. 73(363):618–622.CrossrefGoogle Scholar
  • Bates JM, Granger CWJ (1969) The combination of forecasts. Oper. Res. Quart. 451–468.CrossrefGoogle Scholar
  • Casella G, Berger RL (2002) Statistical Inference, 2nd ed. (Duxbury Press, Pacific Grove, CA).Google Scholar
  • Chen K, Ying Z, Zhang H, Zhao L (2008) Analysis of least absolute deviation. Biometrika 95(1):107–122.CrossrefGoogle Scholar
  • Comhaire P, Papier F (2015) Syngenta uses a cover optimizer to determine production volumes for its European seed supply chain. Interfaces 45(6):501–513.LinkGoogle Scholar
  • Dolgin E (2009) Maize genome mapped. Nature News 1098.Google Scholar
  • Duchi J (2007) Derivations for Linear Algebra and Optimization. Working paper, University of California, Berkeley, Berkeley, CA.Google Scholar
  • Garthwaite PH, Dickey JM (1985) Double- and single-bisection methods for subjective probability assessment in a location-scale family. J. Econometrics 29(1–2):149–163.CrossrefGoogle Scholar
  • Granger CWJ (1980) Forecasting in Business and Economics (Academic Press).CrossrefGoogle Scholar
  • Härdle WK, Müller M, Sperlich S, Werwatz A (2012) Nonparametric and Semiparametric Models (Springer , New York).Google Scholar
  • Harter HL (1977) Nonuniqueness of least absolute values regression. Comm. Statist.-Theory and Methods 6(9):829–838.CrossrefGoogle Scholar
  • Johnson D (1998) The robustness of mean and variance approximations in risk analysis. J. Oper. Res. Soc. 49(3):253–262.CrossrefGoogle Scholar
  • Johnson NL, Kotz S, Balakrishnan N (1994) Continuous Univariate Distributions, Vol. 1, Wiley Series in Probability and Mathematical Statistics: Applied Probability and Statistics (Wiley, New York).Google Scholar
  • Keefer DL, Bodily SE (1983) Three-point approximations for continuous random variables. Management Sci. 29(5):595–609.LinkGoogle Scholar
  • Kelton WD, Law AM (2006) Simulation Modeling and Analysis, 4th ed. (McGraw Hill, New York).Google Scholar
  • Koehler DJ, Brenner L, Griffin D (2002) The calibration of expert judgment: Heuristics and biases beyond the laboratory. Heuristics and Biases: The Psychology of Intuitive Judgment (Cambridge University Press, New York).CrossrefGoogle Scholar
  • Lau HS, Lau AHL (1998) An improved PERT-type formula for standard deviation. IIE Trans. 30(3):273–275.CrossrefGoogle Scholar
  • Lau HS, Lau AHL, Ho CJ (1998) Improved moment-estimation formulas using more than three subjective fractiles. Management Sci. 44(3):346–351.LinkGoogle Scholar
  • Lau HS, Lau AHL, Kottas JF (1999) Using Tocher’s curve to convert subjective quantile-estimates into a probability distribution function. IIE Trans. 31(3):245–254.CrossrefGoogle Scholar
  • Lau AHL, Lau HS, Zhang Y (1996) A simple and logical alternative for making PERT time estimates. IIE Trans. 28(3):183–192.CrossrefGoogle Scholar
  • Lindley DV (1987) Using expert advice on a skew judgmental distribution. Oper. Res. 35(5):716–721.LinkGoogle Scholar
  • O’Hagan A (1998) Eliciting expert beliefs in substantial practical applications. J. Roy. Statist. Soc.: Ser. D (The Statistician) 47(1):21–35.CrossrefGoogle Scholar
  • O’Hagan A (2006) Uncertain Judgements: Eliciting Experts’ Probabilities, Vol. 35 (John Wiley & Sons, Chichester, UK).CrossrefGoogle Scholar
  • O’Hagan A, Oakley JE (2004) Probability is perfect, but we can’t elicit it perfectly. Reliability Engrg. System Safety 85(1–3):239–248.CrossrefGoogle Scholar
  • Pearson ES, Tukey JW (1965) Approximate means and standard deviations based on distances between percentage points of frequency curves. Biometrika 52(3–4):533.CrossrefGoogle Scholar
  • Perry C, Greig ID (1975) Estimating the mean and variance of subjective distributions in pert and decision analysis. Management Sci. 21(12):1477–1480.LinkGoogle Scholar
  • Ravinder HV, Kleinmuntz DN, Dyer JS (1988) The reliability of subjective probabilities obtained through decomposition. Management Sci. 34(2):186–199.LinkGoogle Scholar
  • Stevens JW, O’Hagan A (2002) Incorporation of genuine prior information in cost-effectiveness analysis of clinical trial data. Internat. J. Tech. Assessment in Health Care 18(04):782–790.CrossrefGoogle Scholar
  • Wallsten TS, Nataf C, Shlomi Y, Tomlinson T (2013) Forecasting values of quantitative variables. Paper presented at SPUDM24, Barcelona, Spain, August 20, 2013.Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.