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Activity Number: 449 - Recent Advances in Change-Point Detection and Segmentation
Type: Invited
Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract #326541 Presentation
Title: Making Change-Point Detection Data-Adaptive
Author(s): Piotr Fryzlewicz*
Companies: London School of Economics
Keywords: changepoint detection; change-point detection; change-points; breakpoints; jump detection; segmentation

We study the canonical problem of a-posteriori detection of multiple change-points in the mean of a piecewise-constant signal observed with noise. Even in this simple set-up, many publicly available state-of-the-art methods struggle for certain classes of signals. In particular, this misperformance is observed in methods that work by minimising a "fit to the data plus a penalty" criterion, the reason being that it is challenging to think of a penalty that works well over a wide range of signal classes. To overcome this issue, we propose a new approach whereby methods "learn" from the data as they proceed, and, as a result, operate differently for different signal classes. As an example of this approach, we revisit our earlier change-point detection algorithm, Wild Binary Segmentation, and make it data-adaptive by equipping it with a mechanism for deciding "on the fly" how many sub-samples of the input data to draw, and where to draw them. We show that this significantly improves the algorithm particularly for signals with frequent change-points.

Authors who are presenting talks have a * after their name.

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