Robust growth mixture models with non-ignorable missingness: models, estimation, selection, and application

DOI10.1016/j.csda.2013.07.036zbMath1471.62129OpenAlexW2096830243WikidataQ56928769 ScholiaQ56928769MaRDI QIDQ1621300

Zhenqiu (Laura) Lu, Zhi Yong Zhang

Publication date: 8 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2013.07.036

zbMATH Keywords

Bayesian methodrobust methodsnon-ignorable missing datagrowth mixture modelsmodel selecting criteria

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Bayesian inference (62F15)

Related Items (5)

BAGEL: a non-ignorable missing value estimation method for mixed attribute datasets ⋮ Bayesian analysis of latent Markov models with non-ignorable missing data ⋮ Editorial: The 2nd special issue on advances in mixture models ⋮ A latent transition analysis model for latent-state-dependent nonignorable missingness ⋮ Comparison of maximum likelihood approach, Diggle–Kenward selection model, pattern mixture model with MAR and MNAR dropout data

Uses Software

• WinBUGS
• rsem

Cites Work

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• Pattern-Mixture Models for Multivariate Incomplete Data
• Modeling the Drop-Out Mechanism in Repeated-Measures Studies
• Longitudinal Data Analysis
• Deviance information criteria for missing data models
• A new look at the statistical model identification

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