Imperial College London
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ProfessorNiallAdams

Faculty of Natural Sciences, Department of Mathematics

Professor of Statistics
 
 
 
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Contact

 

+44 (0)20 7594 8837n.adams Website

 
 
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Location

 

6M55Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Hallgren:2022:10.1007/s11222-022-10176-1,
author = {Hallgren, KL and Heard, NA and Adams, NM},
doi = {10.1007/s11222-022-10176-1},
journal = {Statistics and Computing},
pages = {1--19},
title = {Changepoint detection in non-exchangeable data},
url = {http://dx.doi.org/10.1007/s11222-022-10176-1},
volume = {32},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Changepoint models typically assume the data within each segment are independent and identically distributed conditional on some parameters that change across segments. This construction may be inadequate when data are subject to local correlation patterns, often resulting in many more changepoints fitted than preferable. This article proposes a Bayesian changepoint model that relaxes the assumption of exchangeability within segments. The proposed model supposes data within a segment are m-dependent for some unknown m0 that may vary between segments, resulting in a model suitable for detecting clear discontinuities in data that are subject to different local temporal correlations. The approach is suited to both continuous and discrete data. A novel reversible jump Markov chain Monte Carlo algorithm is proposed to sample from the model; in particular, a detailed analysis of the parameter space is exploited to build proposals for the orders of dependence. Two applications demonstrate the benefits of the proposed model: computer network monitoring via change detection in count data, and segmentation of financial time series.
AU  - Hallgren,KL
AU  - Heard,NA
AU  - Adams,NM
DO  - 10.1007/s11222-022-10176-1
EP  - 19
PY  - 2022///
SN  - 0960-3174
SP  - 1
TI  - Changepoint detection in non-exchangeable data
T2  - Statistics and Computing
UR  - http://dx.doi.org/10.1007/s11222-022-10176-1
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000884738000001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://link.springer.com/article/10.1007/s11222-022-10176-1
UR  - http://hdl.handle.net/10044/1/100905
VL  - 32
ER  -
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