Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Nonparametric estimation of the conditional distribution at regression
  boundary points   Nonparametric regression is a standard statistical tool with increased
importance in the Big Data era. Boundary points pose additional difficulties
but local polynomial regression can be used to alleviate them. Local linear
regression, for example, is easy to implement and performs quite well both at
interior as well as boundary points. Estimating the conditional distribution
function and/or the quantile function at a given regressor point is immediate
via standard kernel methods but problems ensue if local linear methods are to
be used. In particular, the distribution function estimator is not guaranteed
to be monotone increasing, and the quantile curves can "cross". In the paper at
hand, a simple method of correcting the local linear distribution estimator for
monotonicity is proposed, and its good performance is demonstrated via
simulations and real data examples.

31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th