Optimal Statistical Hypothesis Testing for Social Choice
Abstract
We address the following question in this paper: “What are the most robust statistical methods for social choice?” By leveraging the theory of uniformly least favorable distributions in the Neyman-Pearson framework to finite models and randomized tests, we characterize uniformly most powerful (UMP) tests, which is a well-accepted statistical optimality w.r.t. robustness, for testing whether a given alternative is the winner under Mallows’ model and under Condorcet’s model, respectively.
Cite
Text
Xia. "Optimal Statistical Hypothesis Testing for Social Choice." Uncertainty in Artificial Intelligence, 2020.Markdown
[Xia. "Optimal Statistical Hypothesis Testing for Social Choice." Uncertainty in Artificial Intelligence, 2020.](https://mlanthology.org/uai/2020/xia2020uai-optimal/)BibTeX
@inproceedings{xia2020uai-optimal,
title = {{Optimal Statistical Hypothesis Testing for Social Choice}},
author = {Xia, Lirong},
booktitle = {Uncertainty in Artificial Intelligence},
year = {2020},
pages = {570-579},
volume = {124},
url = {https://mlanthology.org/uai/2020/xia2020uai-optimal/}
}