. For (very) sparse nominal data, common goodness-of-fit tests usually fail. Alternative goodness-of-fit tests based on extended empirical Bayes approach and grouping are proposed and their consistency is proved. The performance of the tests is illustrated and compared with classical criteria by Monte Carlo simulations.
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