Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: P-spline smoothing for spatial data collected worldwide   Spatial data collected worldwide at a huge number of locations are frequently
used in environmental and climate studies. Spatial modelling for this type of
data presents both methodological and computational challenges. In this work we
illustrate a computationally efficient non parametric framework to model and
estimate the spatial field while accounting for geodesic distances between
locations. The spatial field is modelled via penalized splines (P-splines)
using intrinsic Gaussian Markov Random Field (GMRF) priors for the spline
coefficients. The key idea is to use the sphere as a surrogate for the Globe,
then build the basis of B-spline functions on a geodesic grid system. The basis
matrix is sparse and so is the precision matrix of the GMRF prior, thus
computational efficiency is gained by construction. We illustrate the approach
on a real climate study, where the goal is to identify the Intertropical
Convergence Zone using high-resolution remote sensing data.

10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph