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  Heterogeneous change point inference.

Pein, F., Sieling, H., & Munk, A. (2017). Heterogeneous change point inference. Journal of the Royal Statistical Society. Series B, Statistical Methodology, 79(4), 1207-1227. doi:10.1111/rssb.12202.

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 Urheber:
Pein, F., Autor
Sieling, H., Autor
Munk, A.1, Autor           
Affiliations:
1Research Group of Statistical Inverse-Problems in Biophysics, MPI for biophysical chemistry, Max Planck Society, ou_1113580              

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Schlagwörter: Change point regression; Deviation bounds; Dynamic programming; Heterogeneous noise; Honest confidence sets; Ion channel recordings; Multiscale methods; Robustness; Scale-dependent critical values
 Zusammenfassung: We propose, a heterogeneous simultaneous multiscale change point estimator called 'H-SMUCE' for the detection of multiple change points of the signal in a heterogeneous Gaussian regression model. A piecewise constant function is estimated by minimizing the number of change points over the acceptance region of a multiscale test which locally adapts to changes in the variance. The multiscale test is a combination of local likelihood ratio tests which are properly calibrated by scale-dependent critical values to keep a global nominal level a, even for finite samples. We show that H-SMUCE controls the error of overestimation and underestimation of the number of change points. For this, new deviation bounds for F-type statistics are derived. Moreover, we obtain confidence sets for the whole signal. All results are non-asymptotic and uniform over a large class of heterogeneous change point models. H-SMUCE is fast to compute, achieves the optimal detection rate and estimates the number of change points at almost optimal accuracy for vanishing signals, while still being robust. We compare H-SMUCE with several state of the art methods in simulations and analyse current recordings of a transmembrane protein in the bacterial outer membrane with pronounced heterogeneity for its states. An R-package is available on line.

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Sprache(n): eng - English
 Datum: 2016-08-192017-09
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1111/rssb.12202
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Titel: Journal of the Royal Statistical Society. Series B, Statistical Methodology
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 79 (4) Artikelnummer: - Start- / Endseite: 1207 - 1227 Identifikator: -