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A spatial skew-Gaussian process with a specified covariance function

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  • Jafari Khaledi, Majid
  • Zareifard, Hamid
  • Boojari, Hossein

Abstract

Building on a parsimonious class of closed skew-normal distributions, the present study aims at developing a covariance-adjusted skew-Gaussian process. The obtained results revealed that the introduction of skewness does not affect the prespecified correlation structure. Moreover, the shape of the marginal distribution is allowed to vary across space, which offers extreme flexibility in capturing skewness. It also enables the skew-Gaussian process to be adopted to the correlation structure of the data, either stationary or non-stationary. Hence, the approach is shown to enjoy both theoretical and practical advantages.

Suggested Citation

  • Jafari Khaledi, Majid & Zareifard, Hamid & Boojari, Hossein, 2023. "A spatial skew-Gaussian process with a specified covariance function," Statistics & Probability Letters, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:stapro:v:192:y:2023:i:c:s0167715222001948
    DOI: 10.1016/j.spl.2022.109681
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    References listed on IDEAS

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    1. Abhirup Datta & Sudipto Banerjee & Andrew O. Finley & Alan E. Gelfand, 2016. "Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 800-812, April.
    2. Sudipto Banerjee & Alan E. Gelfand & Andrew O. Finley & Huiyan Sang, 2008. "Gaussian predictive process models for large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 825-848, September.
    3. Lin, Tsung-I & McLachlan, Geoffrey J. & Lee, Sharon X., 2016. "Extending mixtures of factor models using the restricted multivariate skew-normal distribution," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 398-413.
    4. Alexandra M. Schmidt & Kelly C. M. Gonçalves & Patrícia L. Velozo, 2017. "Spatiotemporal models for skewed processes," Environmetrics, John Wiley & Sons, Ltd., vol. 28(6), September.
    5. Tetsuhisa Miwa & A. J. Hayter & Satoshi Kuriki, 2003. "The evaluation of general non‐centred orthant probabilities," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 223-234, February.
    6. Marc G. Genton & Amanda S. Hering, 2017. "Comments on: Spatiotemporal models for skewed processes," Environmetrics, John Wiley & Sons, Ltd., vol. 28(6), September.
    7. Thaís C. O. Fonseca & Mark F. J. Steel, 2011. "Non-Gaussian spatiotemporal modelling through scale mixing," Biometrika, Biometrika Trust, vol. 98(4), pages 761-774.
    8. Palacios, M. Blanca & Steel, Mark F.J., 2006. "Non-Gaussian Bayesian Geostatistical Modeling," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 604-618, June.
    9. Ganggang Xu & Marc G. Genton, 2017. "Tukey -and- Random Fields," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1236-1249, July.
    10. David Bolin, 2014. "Spatial Matérn Fields Driven by Non-Gaussian Noise," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(3), pages 557-579, September.
    11. Reinaldo B. Arellano‐Valle & Adelchi Azzalini, 2006. "On the Unification of Families of Skew‐normal Distributions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(3), pages 561-574, September.
    12. Zareifard, Hamid & Jafari Khaledi, Majid, 2013. "Non-Gaussian modeling of spatial data using scale mixing of a unified skew Gaussian process," Journal of Multivariate Analysis, Elsevier, vol. 114(C), pages 16-28.
    13. Mahmoudian, Behzad, 2018. "On the existence of some skew-Gaussian random field models," Statistics & Probability Letters, Elsevier, vol. 137(C), pages 331-335.
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