High Dimensional Change Points: Challenges and Some Proposals

Change point analysis is being widely applied in various fields such as economics, finance, engineering, genetics and medical research.The main objective is to detect significant changes in the distribution of a data sequence.The change point problem for low dimensional data is well studied in the literature, however change point detection is challenging in high dimensional situations.The classical methods fail to work in high dimensional data where the number of variables is much larger than the number of observations.This paper discusses some main challenges with high dimensional change points and shows the limitations of the recent methods for high dimensional change points in dealing with such challenges.The paper presents some proposals to address those challenges.