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Max-convolution processes with random shape indicator kernels

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  • Krupskii, Pavel
  • Huser, Raphaël

Abstract

In this paper, we introduce a new class of models for spatial data obtained from max-convolution processes based on indicator kernels with random shape. We show that these models have appealing dependence properties including tail dependence at short distances and independence at long distances. We further consider max-convolutions between such processes and processes with tail independence, in order to separately control the bulk and tail dependence behaviors, and to increase flexibility of the model at longer distances, in particular, to capture intermediate tail dependence. We show how parameters can be estimated using a weighted pairwise likelihood approach, and we conduct an extensive simulation study to show that the proposed inference approach is feasible in relatively high dimensions and it yields accurate parameter estimates in most cases. We apply the proposed methodology to analyze daily temperature maxima measured at 100 monitoring stations in the state of Oklahoma, US. Our results indicate that our proposed model provides a good fit to the data, and that it captures both the bulk and the tail dependence structures accurately.

Suggested Citation

  • Krupskii, Pavel & Huser, Raphaël, 2024. "Max-convolution processes with random shape indicator kernels," Journal of Multivariate Analysis, Elsevier, vol. 203(C).
  • Handle: RePEc:eee:jmvana:v:203:y:2024:i:c:s0047259x24000472
    DOI: 10.1016/j.jmva.2024.105340
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    References listed on IDEAS

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    5. Raphaël Huser & Jennifer L. Wadsworth, 2019. "Modeling Spatial Processes with Unknown Extremal Dependence Class," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(525), pages 434-444, January.
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    8. Raphaël Huser & Marc G. Genton, 2016. "Non-Stationary Dependence Structures for Spatial Extremes," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 470-491, September.
    9. R de Fondeville & A C Davison, 2018. "High-dimensional peaks-over-threshold inference," Biometrika, Biometrika Trust, vol. 105(3), pages 575-592.
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    14. Matthew Sainsbury-Dale & Andrew Zammit-Mangion & Raphaël Huser, 2024. "Likelihood-Free Parameter Estimation with Neural Bayes Estimators," The American Statistician, Taylor & Francis Journals, vol. 78(1), pages 1-14, January.
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