Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Approximate Bayesian Computing for Spatial Extremes   Statistical analysis of max-stable processes used to model spatial extremes
has been limited by the difficulty in calculating the joint likelihood
function. This precludes all standard likelihood-based approaches, including
Bayesian approaches. In this paper we present a Bayesian approach through the
use of approximate Bayesian computing. This circumvents the need for a joint
likelihood function by instead relying on simulations from the (unavailable)
likelihood. This method is compared with an alternative approach based on the
composite likelihood. We demonstrate that approximate Bayesian computing can
result in a lower mean square error than the composite likelihood approach when
estimating the spatial dependence of extremes, though at an appreciably higher
computational cost. We also illustrate the performance of the method with an
application to US temperature data to estimate the risk of crop loss due to an
unlikely freeze event.

26
The categories of this paper are: bayes-an
1
The categories of this paper are: cs
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol