The loss of matrix norm equivalence in big data analysis and the Marchenko-Pastur Law
Date
2024-04-26
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
ORCID
Type
Thesis
Degree Level
Masters
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.
Description
Keywords
Marchenko-Pastur Law
Citation
Degree
Master of Mathematics (M.Math)
Department
Mathematics and Statistics
Program
Mathematics