  The classical approach to analyzing extreme value data is the generalized
Pareto distribution (GPD). When the GPD is used to explain a target variable
with the large dimension of covariates, the shape and scale function of
covariates included in GPD are sometimes modeled using the generalized additive
models (GAM). In contrast to many results of application, there are no
theoretical results on the hybrid technique of GAM and GPD, which motivates us
to develop its asymptotic theory. We provide the rate of convergence of the
estimator of shape and scale functions, as well as its local asymptotic
normality.
