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Analyzing survival data with highly negatively skewed distribution: The Gompertz-sinh family

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  • Kahadawala Cooray
  • Malwane Ananda

Abstract

In this article, we explore a new two-parameter family of distribution, which is derived by suitably replacing the exponential term in the Gompertz distribution with a hyperbolic sine term. The resulting new family of distribution is referred to as the Gompertz-sinh distribution, and it possesses a thicker and longer lower tail than the Gompertz family, which is often used to model highly negatively skewed data. Moreover, we introduce a useful generalization of this model by adding a second shape parameter to accommodate a variety of density shapes as well as nondecreasing hazard shapes. The flexibility and better fitness of the new family, as well as its generalization, is demonstrated by providing well-known examples that involve complete, group, and censored data.

Suggested Citation

  • Kahadawala Cooray & Malwane Ananda, 2010. "Analyzing survival data with highly negatively skewed distribution: The Gompertz-sinh family," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(1), pages 1-11.
    • Handle: RePEc:taf:japsta:v:37:y:2010:i:1:p:1-11
    DOI: 10.1080/02664760802663072
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    Cited by:

    1. Sanku Dey & Emrah Altun & Devendra Kumar & Indranil Ghosh, 2023. "The Reflected-Shifted-Truncated Lomax Distribution: Associated Inference with Applications," Annals of Data Science, Springer, vol. 10(3), pages 805-828, June.
    2. Jiang, R., 2010. "Discrete competing risk model with application to modeling bus-motor failure data," Reliability Engineering and System Safety, Elsevier, vol. 95(9), pages 981-988.
    3. Dey Sanku & Waymyers Sophia & Kumar Devendra, 2020. "The Reflected-Shifted-Truncated Lindley Distribution with Applications," Stochastics and Quality Control, De Gruyter, vol. 35(2), pages 67-77, December.

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