Relaxing the Identically Distributed Assumption in Gaussian Co-Clustering for High Dimensional Data

M P B Gallaugher (1) , C Biernacki , P D Mcnicholas

1 Department of Mathematics and Statistics, McMaster University, Hamilton, Canada

A co-clustering model for continuous data that relaxes the identically distributed assumption within blocks of traditional co-clustering is presented. The proposed model, although allowing more flexibility, still maintains the very high degree of parsimony achieved by traditional co-clustering. A stochastic EM algorithm along with a Gibbs sampler is used for parameter estimation and an ICL criterion is used for model selection. Simulated and real datasets are used for illustration and comparison with traditional co-clustering.

Domaines: Statistiques [stat]; Méthodologie [stat.ME]

M P B Gallaugher, C Biernacki, P D Mcnicholas. Relaxing the Identically Distributed Assumption in Gaussian Co-Clustering for High Dimensional Data. 2018. ⟨hal-01862824v1⟩