Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Weak consistency of the 1-nearest neighbor measure with applications to
  missing data   When data is partially missing at random, imputation and importance weighting
are often used to estimate moments of the unobserved population. In this paper,
we study 1-nearest neighbor (1NN) importance weighting, which estimates moments
by replacing missing data with the complete data that is the nearest neighbor
in the non-missing covariate space. We define an empirical measure, the 1NN
measure, and show that it is weakly consistent for the measure of the missing
data. The main idea behind this result is that the 1NN measure is performing
inverse probability weighting in the limit. We study applications to missing
data and mitigating the impact of covariate shift in prediction tasks.

9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph