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Quantile regression C-vine copula model for spatial extremes

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  • Salaheddine El Adlouni

    (Université de Moncton)

Abstract

A spatial quantile regression model is proposed to estimate the quantile curve for a given probability of non-exceedance, as function of locations and covariates. Canonical vines copulas are considered to represent the spatial dependence structure. The marginal at each location is an asymmetric Laplace distribution where the parameters are functions of the covariates. The full conditional quantile distribution is given using the Joe–Clayton copula. Simulations show the flexibility of the proposed model to estimate the quantiles with special dependence structures. A case study illustrates its applicability to estimate quantiles for spatial temperature anomalies.

Suggested Citation

  • Salaheddine El Adlouni, 2018. "Quantile regression C-vine copula model for spatial extremes," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 94(1), pages 299-317, October.
    • Handle: RePEc:spr:nathaz:v:94:y:2018:i:1:d:10.1007_s11069-018-3389-6
    DOI: 10.1007/s11069-018-3389-6
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    References listed on IDEAS

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