Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Clustering Data with Nonignorable Missingness using Semi-Parametric
  Mixture Models   We are concerned in clustering continuous data sets subject to non-ignorable
missingness. We perform clustering with a specific semi-parametric mixture,
under the assumption of conditional independence given the component. The
mixture model isused for clustering and not for estimating the density of the
full variables (observed and unobserved), thus we do not need other assumptions
on the component distribution neither to specify the missingness mechanism.
Estimation is performed by maximizing an extension of smoothed likelihood
allowing missingness. This optimization is achieved by a
Majorization-Minorization algorithm. We illustrate the relevance of our
approach by numerical experiments. Under mild assumptions, we show the
identifiability of the model defining the distribution of the observed data and
the monotony of the algorithm. We also propose an extension of this new method
to the case of mixed-type data that we illustrate on a real data set.

15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
1
The categories of this paper are: cs
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th