Factor Copula Models for Replicated Spatial Data

By Pavel Krupskii, Raphaël Huser, Marc G. Genton. 2015

91 citations recorded by the source corpus.

ABSTRACT We propose a new copula model that can be used with replicated spatial data. Unlike the multivariate normal copula, the proposed copula is based on the assumption that a common factor exists and affects the joint dependence of all measurements of the process. Moreover, the proposed copula can model tail dependence and tail asymmetry. The model is parameterized in terms of a covariance function that may be chosen from the many models proposed in the literature, such as the Matérn model. For some choice of common factors, the joint copula density is given in closed form and therefore likelihood estimation is very fast. In the general case, one-dimensional numerical integration is needed to calculate the likelihood, but estimation is still reasonably fast even with large datasets. We use simulation studies to show the wide range of dependence structures that can be generated by the proposed model with different choices of common factors. We apply the proposed model to spatial temperature data and compare its performance with some popular geostatistics models. Supplementary materials for this article are available online.

Subfields: Economics and Econometrics, Environmental Engineering, Finance

View Paper: https://www.semanticscholar.org/paper/725c5694da30e5968138ca6f4d3b4b075a67adb6

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Citations in the corpus

Modeling Spatial Processes with Unknown Extremal Dependence Class (2017, background): “Alternatively, R or R̃ may be interpreted as an unobserved latent random factor impacting simultaneously thewhole region S , hence affecting the joint tail characteristics, andmaking a link with the common factor copula models for spatial data introduced by Krupskii, Huser, and Genton (2018).”
Bridging asymptotic independence and dependence in spatial extremes using Gaussian scale mixtures (2016, background): “Krupskii et al. (2017) proposed factor copula models constructed from Gaussian processes with a random mean, which can capture asymptotic dependence or independence.”