Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Prior-free Data Acquisition for Accurate Statistical Estimation   We study a data analyst's problem of acquiring data from self-interested
individuals to obtain an accurate estimation of some statistic of a population,
subject to an expected budget constraint. Each data holder incurs a cost, which
is unknown to the data analyst, to acquire and report his data. The cost can be
arbitrarily correlated with the data. The data analyst has an expected budget
that she can use to incentivize individuals to provide their data. The goal is
to design a joint acquisition-estimation mechanism to optimize the performance
of the produced estimator, without any prior information on the underlying
distribution of cost and data. We investigate two types of estimations:
unbiased point estimation and confidence interval estimation.
  Unbiased estimators: We design a truthful, individually rational, online
mechanism to acquire data from individuals and output an unbiased estimator of
the population mean when the data analyst has no prior information on the
cost-data distribution and individuals arrive in a random order. The
performance of this mechanism matches that of the optimal mechanism, which
knows the true cost distribution, within a constant factor. The performance of
an estimator is evaluated by its variance under the worst-case cost-data
correlation.
  Confidence intervals: We characterize an approximately optimal (within a
factor $2$) mechanism for obtaining a confidence interval of the population
mean when the data analyst knows the true cost distribution at the beginning.
This mechanism is efficiently computable. We then design a truthful,
individually rational, online algorithm that is only worse than the
approximately optimal mechanism by a constant factor. The performance of an
estimator is evaluated by its expected length under the worst-case cost-data
correlation.

11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn