Abstract
Bayesian and likelihood approaches to on-line detecting change points in time series are discussed and applied to analyze biomedical data. Using a linear dynamic model, the Bayesian analysis outputs the conditional posterior probability of a change at time t − 1, given the data up to time t and the status of changes occurred before time t − 1. The likelihood method is based on a change-point regression model and tests whether there is no change-point.
Keywords
Change detectionBayesian probabilityTime pointBayesian linear regressionChange analysisComputer scienceStatisticsPosterior probabilityEconometricsMathematicsBayesian inferenceArtificial intelligenceGeography
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Publication Info
- Year
- 1995
- Type
- article
- Volume
- 24
- Issue
- 5
- Pages
- 1307-1324
- Citations
- 4
- Access
- Closed
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Cite This
Heping Zhang
(1995).
Detecting change points and monitoring biomedical data.
Communication in Statistics- Theory and Methods
, 24
(5)
, 1307-1324.
https://doi.org/10.1080/03610929508831555
Identifiers
- DOI
- 10.1080/03610929508831555