On the categorization of a beta-uniform distribution and its application to the gene expression problem

Based on a set of cutting points we propose to categorize a
beta-uniform (BUM) distribution, and term the categorized
distribution as the C-BUM distribution. We study the
categorization effects by comparing the performance of the maximum
likelihood estimates for both models, for different sets of
cutting points. Using the missing information principle we compute
the BUM and C-BUM theoretical Fisher information matrices to
assess loss of information. Finally, we evaluate the method to a
published cancer gene expression data study.