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- W2242800612 abstract "A hierarchical logistic regression model with nested, discrete random effects is proposed for the unsupervised classification of clustered binary data with non-ignorable missing values. An E-M algorithm is proposed that essentially reduces to the iterative estimation of a set of weighted logistic regressions from two augmented datasets, alternated with weights updating. The proposed approach is exploited on a sample of Chinese older adults, to cluster subjects according to their cognitive impairment and ability to cope with a Mini-Mental State Examination questionnaire." @default.
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- W2242800612 date "2013-01-01" @default.
- W2242800612 modified "2023-10-18" @default.
- W2242800612 title "Model-Based Classification of Clustered Binary Data with Non-ignorable Missing Values" @default.
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- W2242800612 doi "https://doi.org/10.1007/978-3-642-35588-2_15" @default.
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