Department Seminars

General Information

When: Thursdays from 4:00–4:50 p.m.
Where: 1170 TMCB

Dan S. Cooley
Colorado State University
Department of Statistics

2007-11-01

Prediction for Max-Stable Processes via an Approximated Conditional Density

The dependence structure of a max-stable random vector is characterized by its spectral measure. Given 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. We also present a new parametric model for the spectral measure of a multivariate max-stable distribution. This model is used to perform prediction for both a time series and spatial process.