Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Spatial aggregation of local likelihood estimates with applications to
  classification   This paper presents a new method for spatially adaptive local (constant)
likelihood estimation which applies to a broad class of nonparametric models,
including the Gaussian, Poisson and binary response models. The main idea of
the method is, given a sequence of local likelihood estimates (``weak''
estimates), to construct a new aggregated estimate whose pointwise risk is of
order of the smallest risk among all ``weak'' estimates. We also propose a new
approach toward selecting the parameters of the procedure by providing the
prescribed behavior of the resulting estimate in the simple parametric
situation. We establish a number of important theoretical results concerning
the optimality of the aggregated estimate. In particular, our ``oracle'' result
claims that its risk is, up to some logarithmic multiplier, equal to the
smallest risk for the given family of estimates. The performance of the
procedure is illustrated by application to the classification problem. A
numerical study demonstrates its reasonable performance in simulated and
real-life examples.

6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
1
The categories of this paper are: cs
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph