  De Haan and Pereira (2006) provided models for spatial extremes in the case
of stationarity, which depend on just one parameter {\beta} > 0 measuring tail
dependence, and they proposed different estimators for this parameter. This
framework was supplemented in Falk (2011) by establishing local asymptotic
normality (LAN) of a corresponding point process of exceedances above a high
multivariate threshold, yielding in particular asymptotic efficient estimators.
  The estimators investigated in these papers are based on a finite set of
points t1,...,td, at which observations are taken. We generalize this approach
in the context of functional extreme value theory (EVT). This more general
framework allows estimation over some spatial parameter space, i.e., the finite
set of points t1,...,td is replaced by t in [a,b]. In particular, we derive
efficient estimators of {\beta} based on those processes in a sample of iid
processes in C[0,1] which exceed a given threshold function.
