Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adaptive greedy algorithm for moderately large dimensions in kernel
  conditional density estimation   This paper studies the estimation of the conditional density f (x, $\times$)
of Y i given X i = x, from the observation of an i.i.d. sample (X i , Y i)
$\in$ R d , i = 1,. .. , n. We assume that f depends only on r unknown
components with typically r d. We provide an adaptive fully-nonparametric
strategy based on kernel rules to estimate f. To select the bandwidth of our
kernel rule, we propose a new fast iterative algorithm inspired by the Rodeo
algorithm (Wasserman and Lafferty (2006)) to detect the sparsity structure of
f. More precisely, in the minimax setting, our pointwise estimator, which is
adaptive to both the regularity and the sparsity, achieves the quasi-optimal
rate of convergence. Its computational complexity is only O(dn log n).

8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys