Title
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
