Prediction for Max-Stable Processes via an Approximated Conditional Density
نویسندگان
چکیده
The dependence structure of a max-stable random vector is characterized by its spectral measure. Using only the spectral measure, we present a method for approximating the conditional density of an unobserved component of a max-stable random vector given the other components of the vector. The approximated conditional density can be used for prediction. Additionally, we present a new parametric model for the spectral measure of a multivariate maxstable distribution. This model is used to perform prediction for time series and interpolation for spatial applications.
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