Modeling Longitudinal Obesity Data with Intermittent Missingness Using a New Latent Variable Model
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Publication:2816735
DOI10.1080/03610918.2014.889154zbMath1347.62152OpenAlexW2016339335MaRDI QIDQ2816735
Marsha D. Marcus, Feng Dai, Li Qin, Lisa A. Weissfeld, Michele D. Levine
Publication date: 25 August 2016
Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610918.2014.889154
Generalized linear models (logistic models) (62J12) Stochastic approximation (62L20) Applications of statistics to psychology (62P15)
Cites Work
- Modeling Longitudinal Data with Nonignorable Dropouts Using a Latent Dropout Class Model
- Latent Pattern Mixture Models for Informative Intermittent Missing Data in Longitudinal Studies
- Inference and missing data
- A Random Pattern-Mixture Model for Longitudinal Data With Dropouts
- Mixed Effects Logistic Regression Models for Longitudinal Binary Response Data with Informative Drop-Out
- Exploratory latent structure analysis using both identifiable and unidentifiable models
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