Abstract
Analyses of non-normal data and longitudinal data to study changes in variables measured repeatedly over time have received considerable attention in social and psychological research. This paper proposes a dynamic two-level nonlinear structural equation model with covariates for analyzing multivariate longitudinal responses that are mixed continuous and ordered categorical variables. To cope with the non-normal continuous data, the corresponding residual errors at both first-level and second-level models are modeled through a Bayesian semiparametric modeling on the basis of a truncated and centered Dirichlet process with stick-breaking priors. The first-level model is defined for measures taken at each time point nested within individuals for investigating their characteristics that vary with time; while the second level is defined for individuals to assess their characteristics that are invariant with time. An algorithm based on the blocked Gibbs sampler is implemented for estimation of parameters. An efficient model comparison statistic, namely the Lν-measure, is also introduced. Results of a simulation study indicate that the performance of the Bayesian semiparametric estimation is satisfactory. The proposed methodologies are applied to a real longitudinal study concerning cocaine use.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.