Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Quantification under prior probability shift: the ratio estimator and
  its extensions   The quantification problem consists of determining the prevalence of a given
label in a target population. However, one often has access to the labels in a
sample from the training population but not in the target population. A common
assumption in this situation is that of prior probability shift, that is, once
the labels are known, the distribution of the features is the same in the
training and target populations. In this paper, we derive a new lower bound for
the risk of the quantification problem under the prior shift assumption.
Complementing this lower bound, we present a new approximately minimax class of
estimators, ratio estimators, which generalize several previous proposals in
the literature. Using a weaker version of the prior shift assumption, which can
be tested, we show that ratio estimators can be used to build confidence
intervals for the quantification problem. We also extend the ratio estimator so
that it can: (i) incorporate labels from the target population, when they are
available and (ii) estimate how the prevalence of positive labels varies
according to a function of certain covariates.

3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an