Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Toward robust early-warning models: A horse race, ensembles and model
  uncertainty   This paper presents first steps toward robust models for crisis prediction.
We conduct a horse race of conventional statistical methods and more recent
machine learning methods as early-warning models. As individual models are in
the literature most often built in isolation of other methods, the exercise is
of high relevance for assessing the relative performance of a wide variety of
methods. Further, we test various ensemble approaches to aggregating the
information products of the built models, providing a more robust basis for
measuring country-level vulnerabilities. Finally, we provide approaches to
estimating model uncertainty in early-warning exercises, particularly model
performance uncertainty and model output uncertainty. The approaches put
forward in this paper are shown with Europe as a playground. Generally, our
results show that the conventional statistical approaches are outperformed by
more advanced machine learning methods, such as k-nearest neighbors and neural
networks, and particularly by model aggregation approaches through ensemble
learning.

10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
16
The categories of this paper are: stat
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys