Estimation and prediction in sparse and unbalanced tables


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.

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