An on-line change-point detection method

Mboup, Mamadou; Join, Cédric; Fliess, Michel

doi:10.1109/med.2008.4602159

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…all derivatives may change abruptly at a change-point location. Both models were considered in various papers by several approaches, mostly from the perspective of extending smoothing approaches, such as kernel regression (Müller, 1992;Qiu, 1994;Eubank and Speckman, 1994;Qiu, 2003;Gijbels and Goderniaux, 2004), local polynomial regression (Loader, 1996;Spokoiny, 1998;Gijbels et al, , 2007Mboup et al, 2008;Xia and Qiu, 2015;Zhang, 2016), (smoothing) splines (Koo, 1997;Lee, 2002;Miyata and Shen, 2003;Huang and Qiu, 2010;Yang and Song, 2014;Liu et al, 2018), wavelet (Raimondo, 1998;Antoniadis and Gijbels, 2002;Abramovich et al, 2007) and Bayesian (Denison et al, 1998;Ogden, 1999;DiMatteo et al, 2001;Punskaya et al, 2002;Fearnhead, 2005;Moreno et al, 2013; approaches. A helpful review is given in (Qiu, 2005).…”

Section: Related Workmentioning

confidence: 99%

Change-point regression with a smooth additive disturbance

Pein¹

2021

Preprint

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We assume a nonparametric regression model with signals given by the sum of a piecewise constant function ands a smooth function. To detect the change-points and estimate the regression functions, we propose PCpluS, a combination of the fused Lasso and kernel smoothing. In contrast to existing approaches, it explicitly uses the assumption that the signal can be decomposed into a piecewise constant and a smooth function when detecting change-points. This is motivated by several applications and by theoretical results about partial linear model. Tuning parameters are selected by cross-validation. We argue that in this setting minimizing the L 1 -loss is superior to minimizing the L 2 -loss. We also highlight important consequences for cross-validation in piecewise constant change-point regression. Simulations demonstrate that our approach has a small average mean square error and detects change-points well, and we apply the methodology to genome sequencing data to detect copy number variations. Finally, we demonstrate its flexibility by combining it with smoothing splines and by proposing extensions to multivariate and filtered data.

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Section: Related Workmentioning

confidence: 99%

Change-point regression with a smooth additive disturbance

Pein¹

2021

Preprint

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“…Originally, the algebraic detection technique was introduced [9,10] to detect spike locations in EEGs. And thus it can be used to detect signals transients.…”

Section: State Of the Artmentioning

confidence: 99%

Spectrum sensing for cognitive radio exploiting spectrum discontinuities detection

Guibène

Turki

Zayen

et al. 2012

J Wireless Com Network

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This article presents a spectrum sensing algorithm for wideband cognitive radio exploiting sensed spectrum discontinuity properties. Some work has already been investigated by wavelet approach by Giannakis et al., but in this article we investigate an algebraic framework in order to model spectrum discontinuities. The information derived at the level of these irregularities will be exploited in order to derive a spectrum sensing algorithm. The numerical simulation show satisfying results in terms of detection performance and receiver operating characteristics curves as the detector takes into account noise annihilation in its inner structure.

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