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
2013-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
