Model-based Clustering of Count Processes

A model-based clustering method based on Gaussian Cox process is proposed to address the problem of clustering of count process data. The model allows for nonparametric estimation of intensity functions of Poisson processes, while simultaneous clustering count process observations. A logistic Gaussian process transformation is imposed on the intensity functions to enforce smoothness. Maximum likelihood parameter estimation is carried out via the EM algorithm, while model selection is addressed using a cross-validated likelihood approach. The proposed model and methodology are applied to two datasets.