Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Covariance approximation for large multivariate spatial data sets with
  an application to multiple climate model errors   This paper investigates the cross-correlations across multiple climate model
errors. We build a Bayesian hierarchical model that accounts for the spatial
dependence of individual models as well as cross-covariances across different
climate models. Our method allows for a nonseparable and nonstationary
cross-covariance structure. We also present a covariance approximation approach
to facilitate the computation in the modeling and analysis of very large
multivariate spatial data sets. The covariance approximation consists of two
parts: a reduced-rank part to capture the large-scale spatial dependence, and a
sparse covariance matrix to correct the small-scale dependence error induced by
the reduced rank approximation. We pay special attention to the case that the
second part of the approximation has a block-diagonal structure. Simulation
results of model fitting and prediction show substantial improvement of the
proposed approximation over the predictive process approximation and the
independent blocks analysis. We then apply our computational approach to the
joint statistical modeling of multiple climate model errors.

2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
1
The categories of this paper are: cs
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn