Adjusting background noise in cluster analyses of longitudinal data

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• 作者：Shengtong Han^a ; Hongmei Zhang^a ; ^{hzhang6@memphis.edu} ; Wilfried Karmaus^a ; Graham Roberts^b ; Hasan Arshad^c
• 关键词：Dirichlet process ; Clustering ; Bayesian methods ; Longitudinal data
• 刊名：Computational Statistics & Data Analysis
• 出版年：2017
• 出版时间：May 2017
• 年：2017
• 卷：109
• 期：Complete
• 页码：93-104
• 全文大小：580 K
• 卷排序：109

文摘

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.