RMT in Statistics

RMT, or Random Matrix Theory, is a branch of statistics that studies the properties of large matrices with randomly chosen entries. It helps analyze complex systems by understanding the behavior of their data patterns, such as correlations or fluctuations, especially when dealing with high-dimensional data. RMT is used in fields like finance, physics, and engineering to identify meaningful signals amid noise, improve data modeling, and predict the behavior of large, interconnected systems. Its core idea is that certain statistical properties become predictable as matrix size grows, helping distinguish significant information from randomness.