Change-point analysis is often used to detect changes in distribution of a single random sequence. The power of the sequential test can be improved by looking at differences with respect to a positively correlated reference sequence, i.e., by using the so-called paired change-point test. In this contribution, we investigate the possibility of detecting changes with respect to two (or more) reference sequences. Our approach is based on a measure of differences between empirical characteristic functions leading to computationally attractive algorithms suitable for high-dimensional observations.
CITATION STYLE
Hlávka, Z., & Hušková, M. (2019). Doubly Paired Change-Point Analysis. In Springer Proceedings in Mathematics and Statistics (Vol. 294, pp. 143–155). Springer. https://doi.org/10.1007/978-3-030-28665-1_11
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