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Author: Lubke, Gitta H.
Resulting in 2 citations.
1. Dolan, Conor V.
Schmittmann, Verena D.
Lubke, Gitta H.
Neale, Michael C.
Regime Switching in the Latent Growth Curve Mixture Model
Structural Equation Modeling 12,1 (2005): 94-120
Cohort(s): NLSY97
Publisher: Lawrence Erlbaum Associates ==> Taylor & Francis
Keyword(s): Alcohol Use; Growth Curves; Markov chain / Markov model; Modeling, Growth Curve/Latent Trajectory Analysis; Modeling, Mixed Effects

Permission to reprint the abstract has been denied by the publisher.

Bibliography Citation
Dolan, Conor V., Verena D. Schmittmann, Gitta H. Lubke and Michael C. Neale. "Regime Switching in the Latent Growth Curve Mixture Model." Structural Equation Modeling 12,1 (2005): 94-120.
2. Lu, Zhenqiu Laura
Zhang, Zhiyong
Lubke, Gitta H.
Bayesian Inference for Growth Mixture Models with Latent Class Dependent Missing Data
Multivariate Behavioral Research 46,4 (2011): 567-597.
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.
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.