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Generalized spatial beta models for skewed and bimodal proportion data

Author

Listed:
  • Mehdi Homayouni

    (Tarbiat Modares University, Department of Statistics)

  • Majid Jafari Khaledi

    (Tarbiat Modares University, Department of Statistics)

  • Esmaeil Najafi

    (Tarbiat Modares University, Department of Statistics)

  • Hormoz Sohrabi

    (Tarbiat Modares University, Department of Forestry)

Abstract

This article introduces two generalizations of the spatial beta model that can effectively handle skewness and bimodality when analyzing bounded geostatistical data. The first approach is based on a mixture structure, while the second adopts the sinh-Gaussian random field to model spatial random effects. A key advantage of the second approach is its avoidance of using a mixture framework, which leads to fewer parameters and reduces the complexities associated with spatial mixture models. This makes the Bayesian inference implementation much easier. To evaluate the performance of the proposed models, we conducted simulation studies, and a comparison was made with the spatial beta model. The results from a practical example demonstrate that, in contrast to the mixture model, the second approach outperforms the competitor in spatial predictions.

Suggested Citation

  • Mehdi Homayouni & Majid Jafari Khaledi & Esmaeil Najafi & Hormoz Sohrabi, 2026. "Generalized spatial beta models for skewed and bimodal proportion data," Computational Statistics, Springer, vol. 41(1), pages 1-26, January.
    • Handle: RePEc:spr:compst:v:41:y:2026:i:1:d:10.1007_s00180-025-01701-7
    DOI: 10.1007/s00180-025-01701-7
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    References listed on IDEAS

    as
    1. Silvia Ferrari & Francisco Cribari-Neto, 2004. "Beta Regression for Modelling Rates and Proportions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(7), pages 799-815.
    2. Eric Yanchenko & Howard D. Bondell & Brian J. Reich, 2024. "Spatial regression modeling via the R2D2 framework," Environmetrics, John Wiley & Sons, Ltd., vol. 35(2), March.
    3. Becky Tang & Henry A. Frye & Alan E. Gelfand & John A. Silander, 2023. "Zero-Inflated Beta Distribution Regression Modeling," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(1), pages 117-137, March.
    4. Bolin, David & Wallin, Jonas & Lindgren, Finn, 2019. "Latent Gaussian random field mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 130(C), pages 80-93.
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    6. Mehdi Homayouni & Majid Jafari Khaledi, 2025. "Generalized sinh-Gaussian random fields," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 54(13), pages 4162-4190, July.
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