Adjusting background noise in cluster analyses of longitudinal data
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.
Publication Title
Computational Statistics and Data Analysis
Recommended Citation
Han, S., Zhang, H., Karmaus, W., Roberts, G., & Arshad, H. (2017). Adjusting background noise in cluster analyses of longitudinal data. Computational Statistics and Data Analysis, 109, 93-104. https://doi.org/10.1016/j.csda.2016.11.009
