Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multivariate Generalized Gaussian Distribution: Convexity and Graphical
  Models   We consider covariance estimation in the multivariate generalized Gaussian
distribution (MGGD) and elliptically symmetric (ES) distribution. The maximum
likelihood optimization associated with this problem is non-convex, yet it has
been proved that its global solution can be often computed via simple fixed
point iterations. Our first contribution is a new analysis of this likelihood
based on geodesic convexity that requires weaker assumptions. Our second
contribution is a generalized framework for structured covariance estimation
under sparsity constraints. We show that the optimizations can be formulated as
convex minimization as long the MGGD shape parameter is larger than half and
the sparsity pattern is chordal. These include, for example, maximum likelihood
estimation of banded inverse covariances in multivariate Laplace distributions,
which are associated with time varying autoregressive processes.

30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
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The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
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The categories of this paper are: eess
1
The categories of this paper are: cs
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org