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Adjusting background noise in cluster analyses of longitudinal data

Adjusting background noise in cluster analyses of longitudinal data
Adjusting background noise in cluster analyses of longitudinal data

Background noise in cluster analyses can potentially mask the true underlying patterns. To tease out patterns uniquely to certain populations, a Bayesian semi-parametric clustering method is presented. It infers and adjusts background noise. The method is built upon a mixture of the Dirichlet process and a point mass function. Simulations demonstrate the effectiveness of the proposed method. The method is then applied to analyze a longitudinal data set on allergic sensitization and asthma status.

0167-9473
93-104
Han, Shengtong
eee0c306-f700-4012-b9de-97b900034835
Zhang, Hongmei
9f774048-54d6-4321-a252-3887b2c76db0
Karmaus, Wilfried
281d0e53-6b5d-4d38-9732-3981b07cd853
Arshad, Hasan
b90c87e5-8abf-4ef2-aeb9-40b60f824843
Roberts, Graham
ea00db4e-84e7-4b39-8273-9b71dbd7e2f3
Han, Shengtong
eee0c306-f700-4012-b9de-97b900034835
Zhang, Hongmei
9f774048-54d6-4321-a252-3887b2c76db0
Karmaus, Wilfried
281d0e53-6b5d-4d38-9732-3981b07cd853
Arshad, Hasan
b90c87e5-8abf-4ef2-aeb9-40b60f824843
Roberts, Graham
ea00db4e-84e7-4b39-8273-9b71dbd7e2f3

Han, Shengtong, Zhang, Hongmei, Karmaus, Wilfried, Arshad, Hasan and Roberts, Graham (2017) Adjusting background noise in cluster analyses of longitudinal data. Computational Statistics & Data Analysis, 109, 93-104. (doi:10.1016/j.csda.2016.11.009).

Record type: Article

Abstract

Background noise in cluster analyses can potentially mask the true underlying patterns. To tease out patterns uniquely to certain populations, a Bayesian semi-parametric clustering method is presented. It infers and adjusts background noise. The method is built upon a mixture of the Dirichlet process and a point mass function. Simulations demonstrate the effectiveness of the proposed method. The method is then applied to analyze a longitudinal data set on allergic sensitization and asthma status.

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More information

e-pub ahead of print date: 27 November 2016
Published date: May 2017

Identifiers

Local EPrints ID: 429706
URI: http://eprints.soton.ac.uk/id/eprint/429706
ISSN: 0167-9473
PURE UUID: dda16b93-1362-415f-b588-719d846b711b
ORCID for Graham Roberts: ORCID iD orcid.org/0000-0003-2252-1248

Catalogue record

Date deposited: 04 Apr 2019 16:30
Last modified: 26 Nov 2021 02:49

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Contributors

Author: Shengtong Han
Author: Hongmei Zhang
Author: Wilfried Karmaus
Author: Hasan Arshad
Author: Graham Roberts ORCID iD

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