Abstract
If all cell counts $n_{ij}$ of a given $R\times C$ contingency table are positive,
estimates of the expected frequencies mij can be found by applying any
regression estimator to the logarithm of the observed counts. If an $R\times C$
table contains outlier(s), ordinary least squares estimates will be affected
by the outlier(s). Various authors have proposed several robust estimators
sensitive to outliers. In this study, robust estimators were applied to an $R\times C$
contingency table with an outlier to obtain the robust parameter estimates
instead of the maximum likelihood (ML) estimates, and the results were
discussed.