A Nonparametric Bayesian Approach for Longitudinal Nonnormal and Missing Data in Growth Curve Models 204 views

Growth curve models (GCMs) are widely used to analyze longitudinal data. This study highlights two aspects of novelties to GCMs. The first novelty solves a practical issue. The study proposes a nonparametric Bayesian selection GCM to simultaneously handle missing and nonnormal data in longitudinal studies. Bayesian methods are used for the estimation. Multiple imputation and selection model approaches are used to handle the ignorable and non-ignorable missing data, respectively. The second novelty advances methodological theory. The proposed modeling framework is an infinite mixture modeling. The study develops new Bayesian model selection criteria to evaluate infinite mixture models in a Bayesian context and compares the performance of new criteria with existing criteria in Bayesian infinite mixture modeling. A Monte Carlo simulation study is conducted to assess the performance of the proposed modeling approach and the corresponding model selection criteria. Simulation results show that the nonparametric Bayesian GCMs perform better than the traditional normal-distribution-based GCM in analyzing the nonnormal and ignorable missing data. The BNP selection GCM in general outperforms the other two GCMs when data are nonnormal and non-ignorable missing. The new Bayesian model selection criteria have the potential to select the Bayesian infinite mixture models. A real data example using the NLSY97 survey data is provided to illustrate the application of the proposed method and model selection criteria.

PHD (Doctor of Philosophy)

English

All rights reserved (no additional license for public reuse)

2020-07-28