Name
Abstract | ||
|---|---|---|
It is common decision analysis practice to elicit quantiles of continuous uncertainties and then fit a continuous probability distribution to the corresponding probabilityquantile 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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1287/deca.2016.0343 | DECISION ANALYSIS |
Keywords | DocType | Volume |
uncertainty, subjective probability, modeling, decision analysis, quantile function | Journal | 14 |
Issue | ISSN | Citations |
1 | 1545-8490 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christopher C. Hadlock | 1 | 0 | 0.68 |
| J. Eric Bickel | 2 | 111 | 12.96 |