Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Model averaging and dimension selection for the singular value
  decomposition   Many multivariate data analysis techniques for an $m\times n$ matrix $\m Y$
are related to the model $\m Y = \m M +\m E$, where $\m Y$ is an $m\times n$
matrix of full rank and $\m M$ is an unobserved mean matrix of rank $K<
(m\wedge n)$. Typically the rank of $\m M$ is estimated in a heuristic way and
then the least-squares estimate of $\m M$ is obtained via the singular value
decomposition of $\m Y$, yielding an estimate that can have a very high
variance. In this paper we suggest a model-based alternative to the above
approach by providing prior distributions and posterior estimation for the rank
of $\m M$ and the components of its singular value decomposition. In addition
to providing more accurate inference, such an approach has the advantage of
being extendable to more general data-analysis situations, such as inference in
the presence of missing data and estimation in a generalized linear modeling
framework.

23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
1
The categories of this paper are: cs
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics