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The Metalog Distributions

Author

Listed:
  • Thomas W. Keelin

    (Keelin Reeds Partners, Menlo Park, California 94025)

Abstract

The metalog distributions constitute a new system of continuous univariate probability distributions designed for flexibility, simplicity, and ease/speed of use in practice. The system is comprised of unbounded, semibounded, and bounded distributions, each of which offers nearly unlimited shape flexibility compared to previous systems of distributions. Explicit shape-flexibility comparisons are provided. Unlike other distributions that require nonlinear optimization for parameter estimation, the metalog quantile functions and probability density functions have simple closed-form expressions that are quantile parameterized linearly by cumulative-distribution-function data. Applications in fish biology and hydrology show how metalogs may aid data and distribution research by imposing fewer shape constraints than other commonly used distributions. Applications in decision analysis show how the metalog system can be specified with three assessed quantiles, how it facilities Monte Carlo simulation, and how applying it aided an actual decision that would have been made wrongly based on commonly used discrete methods.

Suggested Citation

  • Thomas W. Keelin, 2016. "The Metalog Distributions," Decision Analysis, INFORMS, vol. 13(4), pages 243-277, December.
    • Handle: RePEc:inm:ordeca:v:13:y:2016:i:4:p:243-277
    DOI: 10.1287/deca.2016.0338
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    Citations

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    Cited by:

    1. Julia Reis & Julie Shortridge, 2020. "Impact of Uncertainty Parameter Distribution on Robust Decision Making Outcomes for Climate Change Adaptation under Deep Uncertainty," Risk Analysis, John Wiley & Sons, vol. 40(3), pages 494-511, March.
    2. Christopher C. Hadlock & J. Eric Bickel, 2019. "The Generalized Johnson Quantile-Parameterized Distribution System," Decision Analysis, INFORMS, vol. 16(1), pages 67-85, March.
    3. Nesvold, Erik & Bratvold, Reidar B., 2022. "Debiasing probabilistic oil production forecasts," Energy, Elsevier, vol. 258(C).
    4. Perepolkin, Dmytro & Goodrich, Benjamin & Sahlin, Ullrika, 2021. "The tenets of indirect inference in Bayesian models," OSF Preprints enzgs, Center for Open Science.
    5. Christopher C. Hadlock & J. Eric Bickel, 2017. "Johnson Quantile-Parameterized Distributions," Decision Analysis, INFORMS, vol. 14(1), pages 35-64, March.
    6. Valentyn Khokhlov, 2021. "Conditional Value at Risk and Partial Moments for the Metalog Distributions," Papers 2102.10999, arXiv.org.

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