Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multivariate type G Mat\'ern stochastic partial differential equation
  random fields   For many applications with multivariate data, random field models capturing
departures from Gaussianity within realisations are appropriate. For this
reason, we formulate a new class of multivariate non-Gaussian models based on
systems of stochastic partial differential equations with additive type G noise
whose marginal covariance functions are of Mat\'ern type. We consider four
increasingly flexible constructions of the noise, where the first two are
similar to existing copula-based models. In contrast to these, the latter two
constructions can model non-Gaussian spatial data without replicates.
Computationally efficient methods for likelihood-based parameter estimation and
probabilistic prediction are proposed, and the flexibility of the suggested
models is illustrated by numerical examples and two statistical applications.

24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas