Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Approximate Bayesian inference for multivariate point pattern analysis
  in disease mapping   We present a novel approach for the analysis of multivariate case-control
georeferenced data using Bayesian inference in the context of disease mapping,
where the spatial distribution of different types of cancers is analyzed.
Extending other methodology in point pattern analysis, we propose a
log-Gaussian Cox process for point pattern of cases and the controls, which
accounts for risk factors, such as exposure to pollution sources, and includes
a term to measure spatial residual variation.
  For each disease, its intensity is modeled on a baseline spatial effect
(estimated from both controls and cases), a disease-specific spatial term and
the effects on covariates that account for risk factors. By fitting these
models the effect of the covariates on the set of cases can be assessed, and
the residual spatial terms can be easily compared to detect areas of high risk
not explained by the covariates.
  Three different types of effects to model exposure to pollution sources are
considered. First of all, a fixed effect on the distance to the source. Next,
smooth terms on the distance are used to model non-linear effects by means of a
discrete random walk of order one and a Gaussian process in one dimension with
a Mat\'ern covariance.
  Models are fit using the integrated nested Laplace approximation (INLA) so
that the spatial terms are approximated using an approach based on solving
Stochastic Partial Differential Equations (SPDE). Finally, this new framework
is applied to a dataset of three different types of cancer and a set of
controls from Alcal\'a de Henares (Madrid, Spain). Covariates available include
the distance to several polluting industries and socioeconomic indicators. Our
findings point to a possible risk increase due to the proximity to some of
these industries.

14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol