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Efficient regression analyses with zero-augmented models based on ranking

Author

Listed:
  • Deborah Kanda

    (University of New Mexico)

  • Jingjing Yin

    (Georgia Southern University)

  • Xinyan Zhang

    (Kennesaw State University)

  • Hani Samawi

    (Georgia Southern University)

Abstract

Several zero-augmented models exist for estimation involving outcomes with large numbers of zero. Two of such models for handling count endpoints are zero-inflated and hurdle regression models. In this article, we apply the extreme ranked set sampling (ERSS) scheme in estimation using zero-inflated and hurdle regression models. We provide theoretical derivations showing superiority of ERSS compared to simple random sampling (SRS) using these zero-augmented models. A simulation study is also conducted to compare the efficiency of ERSS to SRS and lastly, we illustrate applications with real data sets.

Suggested Citation

  • Deborah Kanda & Jingjing Yin & Xinyan Zhang & Hani Samawi, 2025. "Efficient regression analyses with zero-augmented models based on ranking," Computational Statistics, Springer, vol. 40(2), pages 601-632, February.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:2:d:10.1007_s00180-024-01503-3
    DOI: 10.1007/s00180-024-01503-3
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    References listed on IDEAS

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