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.

Lagona, F. (2013). Model-based classification of clustered binary data with non-ignorable missing values. In N. Torelli, F. Pesarin, A. Bar-Hen (a cura di), Advances in Theoretical and Applied Statistics (pp. 155-165). BERLIN HEIDELBERG : Springer-Verlag [10.1007/978-3-642-35588-2_15].

Model-based classification of clustered binary data with non-ignorable missing values

LAGONA, Francesco
2013

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.
978-3-642-35587-5
Lagona, F. (2013). Model-based classification of clustered binary data with non-ignorable missing values. In N. Torelli, F. Pesarin, A. Bar-Hen (a cura di), Advances in Theoretical and Applied Statistics (pp. 155-165). BERLIN HEIDELBERG : Springer-Verlag [10.1007/978-3-642-35588-2_15].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/161513
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