Narrowest-over-threshold detection of multiple change points and change-point-like features

Narrowest-over-threshold detection of multiple change points and change-point-like features

We propose a new, generic and flexible methodology for non-parametric function estimation, in which we first estimate the number and locations of any features that may be present in the function and then estimate the function parametrically between each pair of neighbouring detected features. Exampl...

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Veröffentlicht in:Journal of the Royal Statistical Society. Series B (Statistical Methodology). - Blackwell Publishers. - 81(2019), 3, Seite 649-672
1. Verfasser: Baranowski, Rafal (VerfasserIn)
Weitere Verfasser: Chen, Yining, Fryzlewicz, Piotr
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2019
Zugriff auf das übergeordnete Werk:Journal of the Royal Statistical Society. Series B (Statistical Methodology)
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520 |a We propose a new, generic and flexible methodology for non-parametric function estimation, in which we first estimate the number and locations of any features that may be present in the function and then estimate the function parametrically between each pair of neighbouring detected features. Examples of features handled by our methodology include change points in the piecewise constant signal model, kinks in the piecewise linear signal model and other similar irregularities, which we also refer to as generalized change points. Our methodology works with only minor modifications across a range of generalized change point scenarios, and we achieve such a high degree of generality by proposing and using a new multiple generalized change point detection device, termed narrowest-over-threshold (NOT) detection. The key ingredient of the NOT method is its focus on the smallest local sections of the data on which the existence of a feature is suspected. For selected scenarios, we show the consistency and near optimality of the NOT algorithm in detecting the number and locations of generalized change points. The NOT estimators are easy to implement and rapid to compute. Importantly, the NOT approach is easy to extend by the user to tailor to their own needs. Our methodology is implemented in the R package not. 
540 |a © 2019 The Authors 
655 4 |a research-article 
700 1 |a Chen, Yining  |e verfasserin  |4 aut 
700 1 |a Fryzlewicz, Piotr  |e verfasserin  |4 aut 
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