MODELING DEPENDENCE WITHIN JOINT TAIL REGIONS

Standard approaches for modelling dependence within joint tail regions are based on extreme value methods which assume max-stability, a part icular form of joint tail dependence. We develop joint tail models bas ed on a broader class of dependence structure which provides a natural link between max-stable models and weaker forms of dependence includi ng independence and negative association. This approach overcomes many of the problems that are encountered with standard methods and is the basis for a Poisson process representation that generalizes existing bivariate results. We apply the new techniques to simulated and enviro nmental data, and demonstrate the marked advantage that the new approa ch offers for joint tail extrapolation.