HIGHLIGHTS
- who: Emmanuel Pilliat and collaborators from the CNRS, France have published the research: Optimal multiple change-point detection for high-dimensional data, in the Journal: (JOURNAL)
- what: In a general change-point setting the authors provide a generic algorithm for aggregating local homogeneity tests into an estimator of change-points in a time series. Informally, the primary objectives are to detect most if not all change-points while estimating no (or at least very few) spurious change-points. In light of this discussion, the second guarantee the authors aim for is to detect all significant . . .
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