Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A multi-resolution approximation via linear projection for large spatial
  datasets   Recent technical advances in collecting spatial data have been increasing the
demand for methods to analyze large spatial datasets. The statistical analysis
for these types of datasets can provide useful knowledge in various fields.
However, conventional spatial statistical methods, such as maximum likelihood
estimation and kriging, are impractically time-consuming for large spatial
datasets due to the necessary matrix inversions. To cope with this problem, we
propose a multi-resolution approximation via linear projection ($M$-RA-lp). The
$M$-RA-lp conducts a linear projection approach on each subregion whenever a
spatial domain is subdivided, which leads to an approximated covariance
function capturing both the large- and small-scale spatial variations.
Moreover, we elicit the algorithms for fast computation of the log-likelihood
function and predictive distribution with the approximated covariance function
obtained by the $M$-RA-lp. Simulation studies and a real data analysis for air
dose rates demonstrate that our proposed $M$-RA-lp works well relative to the
related existing methods.

7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
9
The categories of this paper are: nlin