Bayesian detection of abnormal segments in multiple time series

Bardwell, Lawrence and Fearnhead, Paul (2017) Bayesian detection of abnormal segments in multiple time series. Bayesian Analysis, 12 (1). pp. 193-218. ISSN 1936-0975

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Abstract

We present a novel Bayesian approach to analysing multiple time-series with the aim of detecting abnormal regions. These are regions where the properties of the data change from some normal or baseline behaviour. We allow for the possibility that such changes will only be present in a, potentially small, subset of the time-series. We develop a general model for this problem, and show how it is possible to accurately and efficiently perform Bayesian inference, based upon recursions that enable independent sampling from the posterior distribution. A motivating application for this problem comes from detecting copy number variation (CNVs), using data from multiple individuals. Pooling information across individuals can increase the power of detecting CNVs, but often a specific CNV will only be present in a small subset of the individuals. We evaluate the Bayesian method on both simulated and real CNV data, and give evidence that this approach is more accurate than a recently proposed method for analysing such data.

Item Type:
Journal Article
Journal or Publication Title:
Bayesian Analysis
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2604
Subjects:
ID Code:
72475
Deposited By:
Deposited On:
22 Jan 2015 10:14
Refereed?:
Yes
Published?:
Published
Last Modified:
05 Jul 2020 04:18