Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A generic approach to nonparametric function estimation with mixed data   In practice, data often contain discrete variables. But most of the popular
nonparametric estimation methods have been developed in a purely continuous
framework. A common trick among practitioners is to make discrete variables
continuous by adding a small amount of noise. We show that this approach is
justified if the noise distribution belongs to a certain class. In this case,
any estimator developed in a purely continuous framework extends naturally to
the mixed data setting. Estimators defined that way will be called continuous
convolution estimators. They are extremely easy to implement and their
asymptotic properties transfer directly from the continuous to the mixed data
setting.

13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
1
The categories of this paper are: cs
26
The categories of this paper are: bayes-an
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The categories of this paper are: funct-an
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The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th