Gaussian likelihood inference on data from trans-Gaussian random fields with Matérn covariance function

Yuan Yan, Marc G. Genton*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Gaussian likelihood inference has been studied and used extensively in both statistical theory and applications due to its simplicity. However, in practice, the assumption of Gaussianity is rarely met in the analysis of spatial data. In this paper, we study the effect of non-Gaussianity on Gaussian likelihood inference for the parameters of the Matérn covariance model. By using Monte Carlo simulations, we generate spatial data from a Tukey g-and-h random field, a flexible trans-Gaussian random field, with the Matérn covariance function, where g controls skewness and h controls tail heaviness. We use maximum likelihood based on the multivariate Gaussian distribution to estimate the parameters of the Matérn covariance function. We illustrate the effects of non-Gaussianity of the data on the estimated covariance function by means of functional boxplots. Thanks to our tailored simulation design, a comparison of the maximum likelihood estimator under both the increasing and fixed domain asymptotics for spatial data is performed. We find that the maximum likelihood estimator based on Gaussian likelihood is overall satisfying and preferable than the non-distribution-based weighted least squares estimator for data from the Tukey g-and-h random field. We also present the result for Gaussian kriging based on Matérn covariance estimates with data from the Tukey g-and-h random field and observe an overall satisfactory performance.

Original languageEnglish (US)
Article numbere2458
Issue number5-6
StatePublished - Aug 1 2018

Bibliographical note

Publisher Copyright:
Copyright © 2017 John Wiley & Sons, Ltd.


  • Gaussian likelihood
  • Matérn covariance function
  • Tukey g-and-h random field
  • heavy tails
  • kriging
  • log-Gaussian random field
  • non-Gaussian random field
  • skewness
  • spatial statistics

ASJC Scopus subject areas

  • Ecological Modeling
  • Statistics and Probability


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