Optimal Statistical Hypothesis Testing for Social Choice

Lirong Xia
Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 124:570-579, 2020.

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 this Paper

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.} }
Endnote
%0 Conference Paper %T Optimal Statistical Hypothesis Testing for Social Choice %A Lirong Xia %B Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI) %C Proceedings of Machine Learning Research %D 2020 %E Jonas Peters %E David Sontag %F pmlr-v124-xia20a %I PMLR %P 570--579 %U https://proceedings.mlr.press/v124/xia20a.html %V 124 %X 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.
APA
Xia, L.. (2020). Optimal Statistical Hypothesis Testing for Social Choice. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), in Proceedings of Machine Learning Research 124:570-579 Available from https://proceedings.mlr.press/v124/xia20a.html.

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