ONLINE COVER |
Traditionally,
statistical procedures have been derived via analytic calculations
whose validity often relies on sample size growing to infinity. We use
tools from deep learning to develop a new approach, adversarial Monte
Carlo meta-learning, for constructing optimal statistical procedures.
Statistical problems are framed as two-player games in which Nature
adversarially selects a distribution that makes it difficult for a
statistician to answer the scientific question using data drawn from
this distribution. The players’ strategies are parameterized via neural
networks, and optimal play is learned by modifying the network weights
over many repetitions of the game. Given sufficient computing time, the
statistician’s strategy is (nearly) optimal at the finite observed
sample size, rather than in the hypothetical scenario where sample size
grows to infinity. In numerical experiments and data examples, this
approach performs favorably compared to standard practice in point
estimation, individual-level predictions, and interval estimation.
INTRODUCTION
Motivation and background
Read more...
Additional resources
Science Advances 26 Feb 2020:
Vol. 6, no. 9, eaaw2140
DOI: 10.1126/sciadv.aaw2140
Source: Science Advances
INTRODUCTION
Motivation and background
In
most scientific disciplines, hypotheses are evaluated by applying
statistical tools to experimental or observational data. Hence, the
science of statistics plays a fundamental role in the process of
scientific discovery, and innovations in statistical methodology have
the potential to enable advances in the broader sciences.
Two
distinct paradigms dominate the statistical landscape: the frequentist
and Bayesian approaches. In the frequentist paradigm, probability
statements describe the behavior of statistical procedures over
independent repetitions of an experiment.Read more...
Additional resources
Science Advances 26 Feb 2020:
Vol. 6, no. 9, eaaw2140
DOI: 10.1126/sciadv.aaw2140
Source: Science Advances