Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: C-mix: a high dimensional mixture model for censored durations, with
  applications to genetic data   We introduce a mixture model for censored durations (C-mix), and develop
maximum likelihood inference for the joint estimation of the time distributions
and latent regression parameters of the model. We consider a high-dimensional
setting, with datasets containing a large number of biomedical covariates. We
therefore penalize the negative log-likelihood by the Elastic-Net, which leads
to a sparse parameterization of the model. Inference is achieved using an
efficient Quasi-Newton Expectation Maximization (QNEM) algorithm, for which we
provide convergence properties. We then propose a score by assessing the
patients risk of early adverse event. The statistical performance of the method
is examined on an extensive Monte Carlo simulation study, and finally
illustrated on three genetic datasets with high-dimensional covariates. We show
that our approach outperforms the state-of-the-art, namely both the CURE and
Cox proportional hazards models for this task, both in terms of C-index and
AUC(t).

19
The categories of this paper are: eess
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
1
The categories of this paper are: cs
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas