IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v192y2023ics0167715222001948.html
   My bibliography  Save this article

A spatial skew-Gaussian process with a specified covariance function

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715222001948
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.spl.2022.109681?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sameh Abdulah & Yuxiao Li & Jian Cao & Hatem Ltaief & David E. Keyes & Marc G. Genton & Ying Sun, 2023. "Large‐scale environmental data science with ExaGeoStatR," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    2. Huang Huang & Sameh Abdulah & Ying Sun & Hatem Ltaief & David E. Keyes & Marc G. Genton, 2021. "Competition on Spatial Statistics for Large Datasets," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(4), pages 580-595, December.
    3. 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.
    4. Mahmoudian, Behzad, 2018. "On the existence of some skew-Gaussian random field models," Statistics & Probability Letters, Elsevier, vol. 137(C), pages 331-335.
    5. Matthias Katzfuss & Joseph Guinness & Wenlong Gong & Daniel Zilber, 2020. "Vecchia Approximations of Gaussian-Process Predictions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(3), pages 383-414, September.
    6. Masoud Faridi & Majid Jafari Khaledi, 2022. "The polar-generalized normal distribution: properties, Bayesian estimation and applications," Statistical Papers, Springer, vol. 63(2), pages 571-603, April.
    7. Caamaño-Carrillo, Christian & Bevilacqua, Moreno & López, Cristian & Morales-Oñate, Víctor, 2024. "Nearest neighbors weighted composite likelihood based on pairs for (non-)Gaussian massive spatial data with an application to Tukey-hh random fields estimation," Computational Statistics & Data Analysis, Elsevier, vol. 191(C).
    8. Vahid Tadayon & Abdolrahman Rasekh, 2019. "Non-Gaussian Covariate-Dependent Spatial Measurement Error Model for Analyzing Big Spatial Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(1), pages 49-72, March.
    9. Marchetti, Yuliya & Nguyen, Hai & Braverman, Amy & Cressie, Noel, 2018. "Spatial data compression via adaptive dispersion clustering," Computational Statistics & Data Analysis, Elsevier, vol. 117(C), pages 138-153.
    10. Philip A. White & Alan E. Gelfand, 2021. "Multivariate functional data modeling with time-varying clustering," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 586-602, September.
    11. Kelly R. Moran & Matthew W. Wheeler, 2022. "Fast increased fidelity samplers for approximate Bayesian Gaussian process regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1198-1228, September.
    12. Matthew J. Heaton & Abhirup Datta & Andrew O. Finley & Reinhard Furrer & Joseph Guinness & Rajarshi Guhaniyogi & Florian Gerber & Robert B. Gramacy & Dorit Hammerling & Matthias Katzfuss & Finn Lindgr, 2019. "A Case Study Competition Among Methods for Analyzing Large Spatial Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 398-425, September.
    13. Jaewoo Park & Sangwan Lee, 2022. "A projection‐based Laplace approximation for spatial latent variable models," Environmetrics, John Wiley & Sons, Ltd., vol. 33(1), February.
    14. Morales-Oñate, Víctor & Crudu, Federico & Bevilacqua, Moreno, 2021. "Blockwise Euclidean likelihood for spatio-temporal covariance models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 176-201.
    15. Xu Chen & Surya T. Tokdar, 2021. "Joint quantile regression for spatial data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(4), pages 826-852, September.
    16. Si Cheng & Bledar A. Konomi & Georgios Karagiannis & Emily L. Kang, 2024. "Recursive nearest neighbor co‐kriging models for big multi‐fidelity spatial data sets," Environmetrics, John Wiley & Sons, Ltd., vol. 35(4), June.
    17. Jialuo Liu & Tingjin Chu & Jun Zhu & Haonan Wang, 2022. "Large spatial data modeling and analysis: A Krylov subspace approach," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 1115-1143, September.
    18. Jorge Castillo-Mateo & Miguel Lafuente & Jesús Asín & Ana C. Cebrián & Alan E. Gelfand & Jesús Abaurrea, 2022. "Spatial Modeling of Day-Within-Year Temperature Time Series: An Examination of Daily Maximum Temperatures in Aragón, Spain," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(3), pages 487-505, September.
    19. Chen, Yewen & Chang, Xiaohui & Luo, Fangzhi & Huang, Hui, 2023. "Additive dynamic models for correcting numerical model outputs," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    20. Zilber, Daniel & Katzfuss, Matthias, 2021. "Vecchia–Laplace approximations of generalized Gaussian processes for big non-Gaussian spatial data," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:stapro:v:192:y:2023:i:c:s0167715222001948. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.