Johnson Quantile-Parameterized Distributions

Christopher C. Hadlock () and J. Eric Bickel ()
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Christopher C. Hadlock: Graduate Program in Operations Research and Industrial Engineering, The University of Texas, Austin, Austin, Texas 78712
J. Eric Bickel: Graduate Program in Operations Research and Industrial Engineering, The University of Texas, Austin, Austin, Texas 78712

Decision Analysis, 2017, vol. 14, issue 1, 35-64

Abstract: It is common decision analysis practice to elicit quantiles of continuous uncertainties and then fit a continuous probability distribution to the corresponding probability-quantile pairs. This process often requires curve fitting and the best-fit distribution will often not honor the assessed points. By strategically extending the Johnson Distribution System, we develop a new distribution system that honors any symmetric percentile triplet of quantile assessments (e.g., the 10th-50th-90th) in conjunction with specified support bounds. Further, our new system is directly parameterized by the assessed quantiles and support bounds, eliminating the need to apply a fitting procedure. Our new system is practical, flexible, and, as we demonstrate, able to match the shapes of numerous commonly named distributions.

Keywords: uncertainty; subjective probability; modeling; decision analysis; quantile function (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (3)

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