Mixture Model-based Statistical Pattern Recognition of Clustered or Longitudinal Data
Shu‐Kay Ng, Geoffrey John McLachlan · 2005
Mixture models implemented via the expectation-maximization (EM) algorithm are being increasingly used in a wide range of problems in statistical pattern recogni-tion. For many applied problems in medical and health re-search, the data collected may exhibit a hierarchical struc-ture. The independence assumption in the maximum likeli-hood (ML) learning of mixture models is no longer valid. Ignoring the correlation between hierarchically structured data can lead to misleading pattern recognition. In this paper, we consider the extension of Gaussian mixtures to incorporate data hierarchies via the linear mixed-effects model (LMM). Clustered and longitudinal data hierarchy settings in medical and biological research are considered. 1.