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Abstract The process leading to partial classification with categorical data is sometimes nonrandom. A particular model accounting for incomplete data, which allows the probability of uncertain classification to depend on category identity, is utilized for an analysis of data obtained from a genetic study on Turner's syndrome. Estimates of population proportions are obtained from maximum likelihood. A method for handling nonrandomly missing data arrayed in contingency tables is discussed. Sensitivity analyses incorporating parameters related to the missing-data mechanism are recommended for estimation and testing.
Erik V. Nordheim (Sat,) studied this question.