Title: CESME: Cluster analysis with latent semiparametric mixture models
Authors: Wen Zhou - Colorado State University (United States) [presenting]
Lyuou Zhang - Colorado State University (United States)
Hui Zou - University of Minnesota (United States)
Lulu Wang - Gilead Sciences (United States)
Abstract: Model-based clustering is one of the most popular statistical approaches for cluster analysis and has been widely applied in traditional exploratory analyses. However, the Gaussian assumption plays a critical role for model-based clustering, which is not true in general and prevents the model-based clustering to be used for data with complex distributions, such as those from omics study or climate experiments. We propose a semiparametric latent model for clustering multivariate data with complex distributions, particular those are far different from Gaussian. The model assumes that the observed random variables are obtained from unknown monotone transformations of latent variables that satisfy the Gaussian mixture distribution. The identifiability of the proposed model is carefully studied. An alternating maximization procedure is developed to estimate the proposed model, whose convergence property is investigated. Beside the theoretical exploration, the proposed method is also numerically assessed through extensive simulations and has demonstrated superior performance compared to most of the contemporary competitors. Real data analysis has also been studied to demonstrate the usage of the proposed method in practice.