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Title: Robust Growth Mixture Models with Non-ignorable Missingness: Models, Estimation, Selection, and Application
Resulting in 1 citation.
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Lu, Zhenqiu Laura Zhang, Zhiyong |
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Robust Growth Mixture Models with Non-ignorable Missingness: Models, Estimation, Selection, and Application Computational Statistics and Data Analysis 71 (March 2014): 220-240. Also: http://www.sciencedirect.com/science/article/pii/S0167947313002818 Cohort(s): NLSY97 Publisher: Elsevier Keyword(s): Bayesian; Missing Data/Imputation; Modeling; Peabody Individual Achievement Test (PIAT- Math); Statistical Analysis Challenges in the analyses of growth mixture models include missing data, outliers, estimation, and model selection. Four non-ignorable missingness models to recover the information due to missing data, and three robust models to reduce the effect of non-normality are proposed. A full Bayesian method is implemented by means of data augmentation algorithm and Gibbs sampling procedure. Model selection criteria are also proposed in the Bayesian context. Simulation studies are then conducted to evaluate the performances of the models, the Bayesian estimation method, and selection criteria under different situations. The application of the models is demonstrated through the analysis of education data on children’s mathematical ability development. The models can be widely applied to longitudinal analyses in medical, psychological, educational, and social research. |
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Bibliography Citation
Lu, Zhenqiu Laura and Zhiyong Zhang. "Robust Growth Mixture Models with Non-ignorable Missingness: Models, Estimation, Selection, and Application." Computational Statistics and Data Analysis 71 (March 2014): 220-240.
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