在纵向数据的聚类分析中调整背景噪声。
Adjusting background noise in cluster analyses of longitudinal data.
作者信息
Han Shengtong, Zhang Hongmei, Karmaus Wilfried, Roberts Graham, Arshad Hasan
机构信息
School of Public Health, University of Memphis, Memphis, TN.
Paediatric Allergy and Respiratory Medicine, University of Southampton, Southampton, UK.
出版信息
Comput Stat Data Anal. 2017 May;109:93-104. doi: 10.1016/j.csda.2016.11.009. Epub 2016 Nov 27.
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.
聚类分析中的背景噪声可能会潜在地掩盖真正的潜在模式。为了梳理出特定人群独有的模式，提出了一种贝叶斯半参数聚类方法。它可以推断并调整背景噪声。该方法基于狄利克雷过程和点质量函数的混合。模拟结果证明了所提方法的有效性。然后将该方法应用于分析关于过敏致敏和哮喘状态的纵向数据集。
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