Generalized plaid models

The problem of biclustering has attracted considerable attention in diverse research areas such as functional genomics, text mining, and market research, where there is a need to simultaneously cluster rows and columns of a data matrix. In this paper, we propose a family of generalized plaid models for biclustering, 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, making the models adaptive to the entire distribution of the noise 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.