Detecting Changes in Covariance via Random Matrix Theory.

A novel method is proposed for detecting changes in the covariance structure of moderate dimensional time series. This nonlinear test statistic has a number of useful properties. Most importantly, it is independent of the underlying structure of the covariance matrix. We discuss how results from Random Matrix Theory, can be used to study the behavior of our test statistic in a moderate dimensional setting (i.e., the number of variables is comparable to the length of the data). In particular, we demonstrate that the test statistic converges point wise to a normal distribution under the null hypothesis. We evaluate the performance of the proposed approach on a range of simulated datasets and find that it outperforms a range of alternative recently proposed methods. Finally, we use our approach to study changes in the amount of water on the surface of a plot of soil which feeds into model development for degradation of surface piping. [ABSTRACT FROM AUTHOR]