Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A novel Bayesian approach for variable selection in linear regression
  models   We propose a novel Bayesian approach to the problem of variable selection in
multiple linear regression models. In particular, we present a hierarchical
setting which allows for direct specification of a-priori beliefs about the
number of nonzero regression coefficients as well as a specification of beliefs
that given coefficients are nonzero. To guarantee numerical stability, we adopt
a $g$-prior with an additional ridge parameter for the unknown regression
coefficients. In order to simulate from the joint posterior distribution an
intelligent random walk Metropolis-Hastings algorithm which is able to switch
between different models is proposed. Testing our algorithm on real and
simulated data illustrates that it performs at least on par and often even
better than other well-established methods. Finally, we prove that under some
nominal assumptions, the presented approach is consistent in terms of model
selection.

23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas