A multiple filter test for the detection of rate changes in renewal processes with varying variance

• Verfasst von: Messer, Michael [VerfasserIn]
• Verfasst von: Kirchner, Marietta [VerfasserIn]
• Titel: A multiple filter test for the detection of rate changes in renewal processes with varying variance
• Verf.angabe: Michael Messer, Marietta Kirchner, Julia Schiemann, Jochen Roeper, Ralph Neininger and Gaby Schneider
• E-Jahr: 2014
• Jahr: 19 December 2014
• Umfang: 41 S.
• Fussnoten: Gesehen am 28.10.2020
• Titel Quelle: Enthalten in: The annals of applied statistics
• Ort Quelle: Beachwood, Ohio : Inst. of Mathematical Statistics (IMS), 2007
• Jahr Quelle: 2014
• Band/Heft Quelle: 8(2014), 4, Seite 2027-2067
• ISSN Quelle: 1941-7330
• DOI: doi:10.1214/14-AOAS782
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: change point detection
• Sach-SW: multiple filters
• Sach-SW: multiple time scales
• Sach-SW: nonstationary rate
• Sach-SW: renewal processes
• Sach-SW: Stochastic processes
• K10plus-PPN: 1736873539

Abstract

Nonstationarity of the event rate is a persistent problem in modeling time series of events, such as neuronal spike trains. Motivated by a variety of patterns in neurophysiological spike train recordings, we define a general class of renewal processes. This class is used to test the null hypothesis of stationary rate versus a wide alternative of renewal processes with finitely many rate changes (change points). Our test extends ideas from the filtered derivative approach by using multiple moving windows simultaneously. To adjust the rejection threshold of the test, we use a Gaussian process, which emerges as the limit of the filtered derivative process. We also develop a multiple filter algorithm, which can be used when the null hypothesis is rejected in order to estimate the number and location of change points. We analyze the benefits of multiple filtering and its increased detection probability as compared to a single window approach. Application to spike trains recorded from dopamine midbrain neurons in anesthetized mice illustrates the relevance of the proposed techniques as preprocessing steps for methods that assume rate stationarity. In over 70% of all analyzed spike trains classified as rate nonstationary, different change points were detected by different window sizes.