Statistical change detection for multi-dimensional data

This paper deals with detecting change of distribution in multi-dimensional data sets. We use sequential hypothesis testing methods from statistics to define a general, Monte-Carlo framework for solving this problem. We also define a specific statistical test for distributional change within the framework, that we call the density test. Our experimental results show that the density test has substantially more power than the two existing methods for multi-dimensional change detection.