Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bayesian variable selection with shrinking and diffusing priors   We consider a Bayesian approach to variable selection in the presence of high
dimensional covariates based on a hierarchical model that places prior
distributions on the regression coefficients as well as on the model space. We
adopt the well-known spike and slab Gaussian priors with a distinct feature,
that is, the prior variances depend on the sample size through which
appropriate shrinkage can be achieved. We show the strong selection consistency
of the proposed method in the sense that the posterior probability of the true
model converges to one even when the number of covariates grows nearly
exponentially with the sample size. This is arguably the strongest selection
consistency result that has been available in the Bayesian variable selection
literature; yet the proposed method can be carried out through posterior
sampling with a simple Gibbs sampler. Furthermore, we argue that the proposed
method is asymptotically similar to model selection with the $L_0$ penalty. We
also demonstrate through empirical work the fine performance of the proposed
approach relative to some state of the art alternatives.

13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
1
The categories of this paper are: cs
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th