Adjusting background noise in cluster analyses of longitudinal data

Shengtong Han, Hongmei Zhang, Wilfried Karmaus, Graham Roberts and Hasan Arshad

Computational Statistics & Data Analysis, 2017, vol. 109, issue C, 93-104

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.

Keywords: Dirichlet process; Clustering; Bayesian methods; Longitudinal data (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:109:y:2017:i:c:p:93-104

DOI: 10.1016/j.csda.2016.11.009

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