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The General Segmented Distribution

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  • Michael Vander Wielen
  • Ryan Vander Wielen

Abstract

We develop a distribution supported on a bounded interval with a probability density function that is constructed from any finite number of linear segments. With an increasing number of segments, the distribution can approach any continuous density function of arbitrary form. The flexibility of the distribution makes it a useful tool for various modeling purposes. We further demonstrate that it is capable of fitting data with considerable precision—outperforming distributions recommended by previous studies. We suggest that this distribution is particularly effective in fitting data with sufficient observations that are skewed and multimodal.

Suggested Citation

  • Michael Vander Wielen & Ryan Vander Wielen, 2015. "The General Segmented Distribution," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(10), pages 1994-2009, May.
    • Handle: RePEc:taf:lstaxx:v:44:y:2015:i:10:p:1994-2009
    DOI: 10.1080/03610926.2012.758743
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    Cited by:

    1. Ryan J. Vander Wielen & Michael J. Vander Wielen, 2020. "Unpacking the unknown: a method for identifying status quo distributions," Public Choice, Springer, vol. 182(1), pages 49-72, January.
    2. Christopher C. Hadlock & J. Eric Bickel, 2017. "Johnson Quantile-Parameterized Distributions," Decision Analysis, INFORMS, vol. 14(1), pages 35-64, March.

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