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A factor mixture model for analyzing heterogeneity and cognitive structure of dementia

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  • Silvia Cagnone
  • Cinzia Viroli

Abstract

The Health and Retirement Study (HRS) is funded by the National Institute on Aging of US with the aim of investigating the health, social and economic implications of the aging of the American population. The participants of the study receive a thorough in-home clinical and neuropsychological assessment leading to a diagnosis of normal, cognitive impairment but not demented, or dementia. Due to the heterogeneity of the participants into three classes, we analyze some overall cognitive functioning responses through a factor mixture analysis model. The model extends recent proposals developed for binary and continuous data to general mixed data and to the situation of observed heterogeneity, typical of the HRS study. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Silvia Cagnone & Cinzia Viroli, 2014. "A factor mixture model for analyzing heterogeneity and cognitive structure of dementia," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 98(1), pages 1-20, January.
    • Handle: RePEc:spr:alstar:v:98:y:2014:i:1:p:1-20
    DOI: 10.1007/s10182-012-0206-5
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    References listed on IDEAS

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    Cited by:

    1. Silvia Cagnone & Cinzia Viroli, 2018. "Multivariate latent variable transition models of longitudinal mixed data: an analysis on alcohol use disorder," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1399-1418, November.
    2. Robin Fuchs & Denys Pommeret & Cinzia Viroli, 2022. "Mixed Deep Gaussian Mixture Model: a clustering model for mixed datasets," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(1), pages 31-53, March.
    3. Leila Amiri & Mojtaba Khazaei & Mojtaba Ganjali, 2018. "A mixture latent variable model for modeling mixed data in heterogeneous populations and its applications," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(1), pages 95-115, January.

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