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Maximum composite likelihood estimation for spatial extremes models of Brown–Resnick type with application to precipitation data

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  • Moosup Kim
  • Sangyeol Lee

Abstract

In this study, we consider the maximum composite likelihood estimator for spatial extremes model class of Brown–Resnick type. The composite likelihood is constructed based on the weighted tail empirical process. It is shown that the proposed estimator is consistent and asymptotically normal under some regularity conditions fulfilled by the model class. We conduct Monte Carlo simulations to evaluate the estimator and apply it to the analysis of a precipitation data set.

Suggested Citation

  • Moosup Kim & Sangyeol Lee, 2022. "Maximum composite likelihood estimation for spatial extremes models of Brown–Resnick type with application to precipitation data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 1023-1059, September.
    • Handle: RePEc:bla:scjsta:v:49:y:2022:i:3:p:1023-1059
    DOI: 10.1111/sjos.12551
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    1. Cristiano Varin & Paolo Vidoni, 2005. "A note on composite likelihood inference and model selection," Biometrika, Biometrika Trust, vol. 92(3), pages 519-528, September.
    2. Einmahl, J.H.J. & de Haan, L.F.M. & Li, D., 2006. "Weighted approximations of tail copula processes with applications to testing the bivariate extreme value condition," Other publications TiSEM 18b65ac3-ba79-4bff-ad53-2, Tilburg University, School of Economics and Management.
    3. José A. F. Machado & Paulo Parente, 2005. "Bootstrap estimation of covariance matrices via the percentile method," Econometrics Journal, Royal Economic Society, vol. 8(1), pages 70-78, March.
    4. Jennifer L. Wadsworth & Jonathan A. Tawn, 2014. "Efficient inference for spatial extreme value processes associated to log-Gaussian random functions," Biometrika, Biometrika Trust, vol. 101(1), pages 1-15.
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