New directions in algebraic statistics: Three challenges from 2023
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909413714952192 |
|---|---|
| author | Alexandr, Yulia Bakenhus, Miles Curiel, Mark Deshpande, Sameer K. Gross, Elizabeth Gu, Yuqi Hill, Max Johnson, Joseph Kagy, Bryson Karwa, Vishesh Li, Jiayi Lyu, Hanbaek Petrović, Sonja Rodriguez, Jose Israel |
| author_facet | Alexandr, Yulia Bakenhus, Miles Curiel, Mark Deshpande, Sameer K. Gross, Elizabeth Gu, Yuqi Hill, Max Johnson, Joseph Kagy, Bryson Karwa, Vishesh Li, Jiayi Lyu, Hanbaek Petrović, Sonja Rodriguez, Jose Israel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_13961 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | New directions in algebraic statistics: Three challenges from 2023 Alexandr, Yulia Bakenhus, Miles Curiel, Mark Deshpande, Sameer K. Gross, Elizabeth Gu, Yuqi Hill, Max Johnson, Joseph Kagy, Bryson Karwa, Vishesh Li, Jiayi Lyu, Hanbaek Petrović, Sonja Rodriguez, Jose Israel Statistics Theory 62R01 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. |
| title | New directions in algebraic statistics: Three challenges from 2023 |
| topic | Statistics Theory 62R01 |
| url | https://arxiv.org/abs/2402.13961 |