  Focusing on regression based analysis of extremes in a presence of
systematically missing covariates, this work presents a data-driven
spatio-temporal regression based clustering of threshold excesses. It is shown
that in a presence of systematically missing covariates the behavior of
threshold excesses becomes nonstationary and nonhomogenous. The presented
approach describes this complex behavior by a set of local stationary
Generalized Pareto Distribution (GPD) models, where the parameters are
expressed as regression models, and a latent spatio-temporal switching process.
The spatio-temporal switching process is resolved by the nonparametric Finite
Element Methodology for time series analysis with Bounded Variation of the
model parameters (FEM-BV). The presented FEM-BV-GPD approach goes beyond strong
a priori assumptions made in standard latent class models like Mixture Models
and Hidden Markov Models. In addition, it provides a pragmatic description of
the underlying dependency structure. The performance of the framework is
demonstrated on historical precipitation data for Switzerland and compared with
the results obtained by the standard methods on the same data.
