Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Estimation of the Covariance Matrix of Large Dimensional Data   This paper deals with the problem of estimating the covariance matrix of a
series of independent multivariate observations, in the case where the
dimension of each observation is of the same order as the number of
observations. Although such a regime is of interest for many current
statistical signal processing and wireless communication issues, traditional
methods fail to produce consistent estimators and only recently results relying
on large random matrix theory have been unveiled. In this paper, we develop the
parametric framework proposed by Mestre, and consider a model where the
covariance matrix to be estimated has a (known) finite number of eigenvalues,
each of it with an unknown multiplicity. The main contributions of this work
are essentially threefold with respect to existing results, and in particular
to Mestre's work: To relax the (restrictive) separability assumption, to
provide joint consistent estimates for the eigenvalues and their
multiplicities, and to study the variance error by means of a Central Limit
theorem.

28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
22
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: eess
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The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
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The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th