Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robustness analysis of a Maximum Correntropy framework for linear
  regression   In this paper we formulate a solution of the robust linear regression problem
in a general framework of correntropy maximization. Our formulation yields a
unified class of estimators which includes the Gaussian and Laplacian
kernel-based correntropy estimators as special cases. An analysis of the
robustness properties is then provided. The analysis includes a quantitative
characterization of the informativity degree of the regression which is
appropriate for studying the stability of the estimator. Using this tool, a
sufficient condition is expressed under which the parametric estimation error
is shown to be bounded. Explicit expression of the bound is given and
discussion on its numerical computation is supplied. For illustration purpose,
two special cases are numerically studied.

21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
16
The categories of this paper are: stat
27
The categories of this paper are: comp-gas