Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Minimizing Uncertainty in Prevalence Estimates   Estimating prevalence, the fraction of a population with a certain medical
condition, is fundamental to epidemiology. Traditional methods rely on
classification of test samples taken at random from a population. Such
approaches to estimating prevalence are biased and have uncontrolled
uncertainty. Here, we construct a new, unbiased, minimum variance estimator for
prevalence. Recent result show that prevalence can be estimated from counting
arguments that compare the fraction of samples in an arbitrary subset of the
measurement space to what is expected from conditional probability models of
the diagnostic test. The variance of this estimator depends on both the choice
of subset and the fraction of samples falling in it. We employ a bathtub
principle to recast variance minimization as a one-dimensional optimization
problem. Using symmetry properties, we show that the resulting objective
function is well-behaved and can be numerically minimized.

22
The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph