The loss of matrix norm equivalence in big data analysis and the Marchenko-Pastur Law
| dc.contributor.advisor | Wang, JC | |
| dc.contributor.committeeMember | Li, Longhai | |
| dc.contributor.committeeMember | Xing, Li | |
| dc.contributor.committeeMember | Rayan, Steven | |
| dc.creator | Heidorn, Emma Fan | |
| dc.date.accessioned | 2024-04-26T20:48:22Z | |
| dc.date.available | 2024-04-26T20:48:22Z | |
| dc.date.copyright | 2024 | |
| dc.date.created | 2024-04 | |
| dc.date.issued | 2024-04-26 | |
| dc.date.submitted | April 2024 | |
| dc.date.updated | 2024-04-26T20:48:22Z | |
| dc.description.abstract | In statistics, p dimensional data are collected n times. Traditionally, the dimension of p would be larger than n; however, as technology progresses, we enter the era of big data where n is no longer much larger than p. The large ratio of p n causes pitfalls in methods and algorithms that were developed with the opposite in mind. To solve this problem, methods using random matrix theory were brought up in [4], this thesis will be focusing on results concerning the Marchenko-Pastur Law. This thesis is not a cutting-edge research, but an organized presentation of the Marchenko- Pastur Law. This is written so students and researchers can quickly grasp the ideas and methods without difficulty. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | https://hdl.handle.net/10388/15647 | |
| dc.language.iso | en | |
| dc.subject | Marchenko-Pastur Law | |
| dc.title | The loss of matrix norm equivalence in big data analysis and the Marchenko-Pastur Law | |
| dc.type | Thesis | |
| dc.type.material | text | |
| thesis.degree.department | Mathematics and Statistics | |
| thesis.degree.discipline | Mathematics | |
| thesis.degree.grantor | University of Saskatchewan | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Mathematics (M.Math) |