Abstract
In many practical situations, investigators are forced to study the structure underlying the crossclassification of several categorical variables via tables of observed counts in which the observations corresponding to certain sets of cells are indistinguishable. Methods are presented for the analysis of such contingency tables with incompletely cross-classified data via loglinear models. The method of maximum likelihood is used to estimate the expected cell counts which are then used to test the goodness-of-fit of the model. Extensions to incomplete (or truncated) contingency tables are indicated and several examples are given.
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