Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A convex pseudo-likelihood framework for high dimensional partial
  correlation estimation with convergence guarantees   Sparse high dimensional graphical model selection is a topic of much interest
in modern day statistics. A popular approach is to apply l1-penalties to either
(1) parametric likelihoods, or, (2) regularized regression/pseudo-likelihoods,
with the latter having the distinct advantage that they do not explicitly
assume Gaussianity. As none of the popular methods proposed for solving
pseudo-likelihood based objective functions have provable convergence
guarantees, it is not clear if corresponding estimators exist or are even
computable, or if they actually yield correct partial correlation graphs. This
paper proposes a new pseudo-likelihood based graphical model selection method
that aims to overcome some of the shortcomings of current methods, but at the
same time retain all their respective strengths. In particular, we introduce a
novel framework that leads to a convex formulation of the partial covariance
regression graph problem, resulting in an objective function comprised of
quadratic forms. The objective is then optimized via a coordinate-wise
approach. The specific functional form of the objective function facilitates
rigorous convergence analysis leading to convergence guarantees; an important
property that cannot be established using standard results, when the dimension
is larger than the sample size, as is often the case in high dimensional
applications. These convergence guarantees ensure that estimators are
well-defined under very general conditions, and are always computable. In
addition, the approach yields estimators that have good large sample properties
and also respect symmetry. Furthermore, application to simulated/real data,
timing comparisons and numerical convergence is demonstrated. We also present a
novel unifying framework that places all graphical pseudo-likelihood methods as
special cases of a more general formulation, leading to important insights.

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The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
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The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
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The categories of this paper are: atom-ph
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
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The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
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The categories of this paper are: supr-con
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The categories of this paper are: hep-th
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The categories of this paper are: math-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: q-fin
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The categories of this paper are: chem-ph
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The categories of this paper are: alg-geom