Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Consistency, breakdown robustness, and algorithms for robust improper
  maximum likelihood clustering   The robust improper maximum likelihood estimator (RIMLE) is a new method for
robust multivariate clustering finding approximately Gaussian clusters. It
maximizes a pseudo-likelihood defined by adding a component with improper
constant density for accommodating outliers to a Gaussian mixture. A special
case of the RIMLE is MLE for multivariate finite Gaussian mixture models. In
this paper we treat existence, consistency, and breakdown theory for the RIMLE
comprehensively. RIMLE's existence is proved under non-smooth covariance matrix
constraints. It is shown that these can be implemented via a computationally
feasible Expectation-Conditional Maximization algorithm.

2
The categories of this paper are: math
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph