Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Nonparametric Bayesian Lomax delegate racing for survival analysis with
  competing risks   We propose Lomax delegate racing (LDR) to explicitly model the mechanism of
survival under competing risks and to interpret how the covariates accelerate
or decelerate the time to event. LDR explains non-monotonic covariate effects
by racing a potentially infinite number of sub-risks, and consequently relaxes
the ubiquitous proportional-hazards assumption which may be too restrictive.
Moreover, LDR is naturally able to model not only censoring, but also missing
event times or event types. For inference, we develop a Gibbs sampler under
data augmentation for moderately sized data, along with a stochastic gradient
descent maximum a posteriori inference algorithm for big data applications.
Illustrative experiments are provided on both synthetic and real datasets, and
comparison with various benchmark algorithms for survival analysis with
competing risks demonstrates distinguished performance of LDR.

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The categories of this paper are: math
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The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
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The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
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The categories of this paper are: adap-org
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-fin