Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On Semiparametric Exponential Family Graphical Models   We propose a new class of semiparametric exponential family graphical models
for the analysis of high dimensional mixed data. Different from the existing
mixed graphical models, we allow the nodewise conditional distributions to be
semiparametric generalized linear models with unspecified base measure
functions. Thus, one advantage of our method is that it is unnecessary to
specify the type of each node and the method is more convenient to apply in
practice. Under the proposed model, we consider both problems of parameter
estimation and hypothesis testing in high dimensions. In particular, we propose
a symmetric pairwise score test for the presence of a single edge in the graph.
Compared to the existing methods for hypothesis tests, our approach takes into
account of the symmetry of the parameters, such that the inferential results
are invariant with respect to the different parametrizations of the same edge.
Thorough numerical simulations and a real data example are provided to back up
our results.

27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int