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arXiv · 2402.13961

New directions in algebraic statistics: Three challenges from 2023

Yulia Alexandr·Miles Bakenhus·Mark Curiel·Sameer K. Deshpande·Elizabeth Gross·Yuqi Gu·Max Hill·Joseph Johnson·Bryson Kagy·Vishesh Karwa·Jiayi Li·Hanbaek Lyu·Sonja Petrović·Jose Israel Rodriguez
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Abstract

In the last quarter of a century, algebraic statistics has established itself as an expanding field which uses multilinear algebra, commutative algebra, computational algebra, geometry, and combinatorics to tackle problems in mathematical statistics. These developments have found applications in a growing number of areas, including biology, neuroscience, economics, and social sciences. Naturally, new connections continue to be made with other areas of mathematics and statistics. This paper outlines three such connections: to statistical models used in educational testing, to a classification problem for a family of nonparametric regression models, and to phase transition phenomena under uniform sampling of contingency tables. We illustrate the motivating problems, each of which is for algebraic statistics a new direction, and demonstrate an enhancement of related methodologies.

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Yulia Alexandr, Miles Bakenhus, Mark Curiel, Sameer K. Deshpande, Elizabeth Gross, Yuqi Gu, Max Hill, Joseph Johnson, Bryson Kagy, Vishesh Karwa, Jiayi Li, Hanbaek Lyu, Sonja Petrović, Jose Israel Rodriguez. 2024-02-21. New directions in algebraic statistics: Three challenges from 2023. https://doi.org/10.2140/astat.2024.15.357

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