When there is a multi-way table where each dimension has large number of levels, it is computationally intensive to fit even the standard mixed effects models. We propose a novel hierarchical ANOVA representation for such data. Modeling back-fitting requires repeated calculations of sub-table means, which can be efficiently computed when observations are sparse.