Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bayesian inference on dependence in multivariate longitudinal data   In many applications, it is of interest to assess the dependence structure in
multivariate longitudinal data. Discovering such dependence is challenging due
to the dimensionality involved. By concatenating the random effects from
component models for each response, dependence within and across longitudinal
responses can be characterized through a large random effects covariance
matrix. Motivated by the common problems in estimating this matrix, especially
the off-diagonal elements, we propose a Bayesian approach that relies on
shrinkage priors for parameters in a modified Cholesky decomposition. Without
adjustment, such priors and previous related approaches are order-dependent and
tend to shrink strongly toward an ARtype structure. We propose moment-matching
(MM) priors to mitigate such problems. Efficient Gibbs samplers are developed
for posterior computation. The methods are illustrated through simulated
examples and are applied to a longitudinal epidemiologic study of hormones and
oxidative stress.

14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th
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
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph