UAI 2020poster1 citations
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.
BibTeX
@InProceedings{pmlr-v124-xia20a,
title = {Optimal Statistical Hypothesis Testing for Social Choice},
author = {Xia, Lirong},
booktitle = {Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI)},
pages = {570--579},
year = {2020},
editor = {Peters, Jonas and Sontag, David},
volume = {124},
series = {Proceedings of Machine Learning Research},
month = {03--06 Aug},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v124/xia20a/xia20a.pdf},
url = {https://proceedings.mlr.press/v124/xia20a.html},
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.}
}