Conference on Conditional Independence Structures and Extremes

s of Talks Sven Buhl, Technical University of Munich, Germany Semiparametric estimation for max-stable space-time processes Abstract: We propose a semiparametric estimation procedure based on a closed form expression of the extremogram (cf. [2], [3], [4]) to estimate the model parameters in a max-stable space-time process. We establish the asymptotic properties of the resulting parameter estimates. A simulation study shows that the proposed procedure works well for moderate sample sizes. Finally, we apply this estimation procedure to tting a max-stable model to radar rainfall measurements in a region in Florida. This modeling procedure helps to quantify the extremal properties of the space-time observations. This talk is based on a joint work with Richard Davis, Claudia Klüppelberg and Christina Steinkohl. We propose a semiparametric estimation procedure based on a closed form expression of the extremogram (cf. [2], [3], [4]) to estimate the model parameters in a max-stable space-time process. We establish the asymptotic properties of the resulting parameter estimates. A simulation study shows that the proposed procedure works well for moderate sample sizes. Finally, we apply this estimation procedure to tting a max-stable model to radar rainfall measurements in a region in Florida. This modeling procedure helps to quantify the extremal properties of the space-time observations. This talk is based on a joint work with Richard Davis, Claudia Klüppelberg and Christina Steinkohl. References: [1] S. Buhl, R. A. Davis, C. Klüppelberg and C. Steinkohl (2016). Semiparametric estimation for max-stable space-time processes. In preparation. [2] S. Buhl and C. Klüppelberg (2016). Limit theory for the empirical extremogram of random elds. In preparation. [3] Y. Cho, R. A. Davis and S. Ghosh (2016). Asymptotic Properties of the Empirical Spatial Extremogram. Scandinavian Journal of Statistics. [4] R. A. Davis and T. Mikosch (2009). The extremogram: A correlogram for extreme events. Bernoulli 15(4):977-1009.