Ignorability for categorical data

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We study the problem of ignorability in likelihood-based inference from incomplete categorical data. Two versions of the coarsened at random assumption (car) are distinguished, their compatibility with the parameter distinctness assumption is investigated and several conditions for ignorability that do not require an extra parameter distinctness assumption are established.
Original languageEnglish
JournalAnnals of Statistics
Issue number4
Pages (from-to)1964-1981
Number of pages17
Publication statusPublished - Aug 2005

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