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Author: Lu, Zhenqiu Laura
Resulting in 2 citations.
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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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Lu, Zhenqiu Laura Zhang, Zhiyong Lubke, Gitta H. |
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Bayesian Inference for Growth Mixture Models with Latent Class Dependent Missing Data Multivariate Behavioral Research 46,4 (2011): 567-597. Also: http://www.tandfonline.com/doi/abs/10.1080/00273171.2011.589261 Cohort(s): NLSY97 Publisher: Taylor & Francis Keyword(s): Bayesian; Missing Data/Imputation; Modeling, Growth Curve/Latent Trajectory Analysis Growth mixture models (GMMs) with nonignorable missing data have drawn increasing attention in research communities but have not been fully studied. The goal of this article is to propose and to evaluate a Bayesian method to estimate the GMMs with latent class dependent missing data. An extended GMM is first presented in which class probabilities depend on some observed explanatory variables and data missingness depends on both the explanatory variables and a latent class variable. A full Bayesian method is then proposed to estimate the model. Through the data augmentation method, conditional posterior distributions for all model parameters and missing data are obtained. A Gibbs sampling procedure is then used to generate Markov chains of model parameters for statistical inference. The application of the model and the method is first demonstrated through the analysis of mathematical ability growth data from the National Longitudinal Survey of Youth 1997 (Bureau of Labor Statistics, U.S. Department of Labor, 1997). A simulation study considering 3 main factors (the sample size, the class probability, and the missing data mechanism) is then conducted and the results show that the proposed Bayesian estimation approach performs very well under the studied conditions. Finally, some implications of this study, including the misspecified missingness mechanism, the sample size, the sensitivity of the model, the number of latent classes, the model comparison, and the future directions of the approach, are discussed. |
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Bibliography Citation
Lu, Zhenqiu Laura, Zhiyong Zhang and Gitta H. Lubke. "Bayesian Inference for Growth Mixture Models with Latent Class Dependent Missing Data." Multivariate Behavioral Research 46,4 (2011): 567-597.
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