On-line change-point detection (for state space models) using multi-process Kalman filters
type: Item
instance of: scholarly article
title: On-line change-point detection (for state space models) using multi-process Kalman filters
zbMATH Open document ID: 0959.93054
DOI: 10.1016/S0024-3795(98)10132-5
author: Martin Daumer
author: Markus Falk
published in: Linear Algebra and its Applications
publication date: 1999-11-28T00:00:00Z
review text: Based on multi-process Kalman filters, an algorithm is presented for estimating the probability that a change-point has occurred in a noisy time series. Especially, change-points are considered which arise in biomedical signals, i.e. jumps or drifts in nonstationary time series possibly corrupted by embedded outliers. The stability of the algorithm against outliers is examined, and an example is given.
reviewed by: Kurt Marti
Mathematics Subject Classification ID: 93E10
Mathematics Subject Classification ID: 60K30
zbMATH DE Number: 1371016
zbMATH Keywords: multi-process Kalman filters
zbMATH Keywords: change-point
zbMATH Keywords: noisy time series
zbMATH Keywords: biomedical signals
zbMATH Keywords: embedded outliers
zbMATH Keywords: stability of the algorithm
MaRDI profile type: Publication
cites work: Monitoring Renal Transplants: An Application of the Multiprocess Kalman Filter
cites work: Bayesian forecasting and dynamic models
cites work: Detection of Coseismic Changes of Underground Water Level
Wikidata QID: Q126657447
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description: scientific article; zbMATH DE number 1371016