Generalized plaid models

The problem of two-way clustering has attracted considerable attention in diverse research areas such as functional genomics, text mining, and market research, where people want to simultaneously cluster rows and columns of a data matrix. In this paper, we propose a family of generalized plaid models for two-way clustering, where the layer estimation is regularized by Bayesian Information Criterion (BIC).

The new models have broadened the scope of ordinary plaid models by

specifying the variance function to make the models adaptive to the entire distribution of the error term. A formal test is provided for finding significant layers. A Metropolis algorithm is also developed to calculate the maximum likelihood estimators of unknown parameters in the proposed models. Three simulation studies and the applications to two real datasets are reported, which demonstrate that our procedure is promising.