Detecting change points and monitoring biomedical data |
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Authors: | Heping Zhang |
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Affiliation: | Yale University School of Medicine , New Haven, Connecticut, 06520 |
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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. |
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Keywords: | change point regression likelihood ratio linear dynamic model posterior probability renal transplant |
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