Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Bayesian semi-parametric hybrid model for spatial extremes with
  unknown dependence structure   The max-stable process is an asymptotically justified model for spatial
extremes. In particular, we focus on the hierarchical extreme-value process
(HEVP), which is a particular max-stable process that is conducive to Bayesian
computing. The HEVP and all max-stable process models are parametric and impose
strong assumptions including that all marginal distributions belong to the
generalized extreme value family and that nearby sites are asymptotically
dependent. We generalize the HEVP by relaxing these assumptions to provide a
wider class of marginal distributions via a Dirichlet process prior for the
spatial random effects distribution. In addition, we present a hybrid
max-mixture model that combines the strengths of the parametric and
semi-parametric models. We show that this versatile max-mixture model
accommodates both asymptotic independence and dependence and can be fit using
standard Markov chain Monte Carlo algorithms. The utility of our model is
evaluated in Monte Carlo simulation studies and application to Netherlands wind
gust data.

15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
17
The categories of this paper are: q-fin