Mixture multilevel vector-autoregressive modeling

The modeling techniques for intensive longitudinal data are increasingly focused on individual differences.
In my talk I will present mixture multilevel vector-autoregressive modeling, which extends multilevel vectorautoregressive modeling by including a mixture, to identify individuals with similar traits and dynamic processes. This exploratory model identifies mixture components, where each component refers to individuals
with similarities in means (expressing traits), autoregressions, and cross-regressions (expressing dynamics),
while allowing for some inter-individual differences within the components on these attributes. I will illustrate the model using affective data from the COGITO study. These data consist of samples for two different age
groups of over 100 individuals each who were measured for about 100 days. I will demonstrate the advantage
of exploratory identifying mixture components by analyzing these heterogeneous samples jointly.