Presenters: Zachary Fisher (Penn State University)
Data rising from high-dimensional time-dependent systems is increasingly common in the health, social and behavioral sciences. Despite the many benefits these data provide, relatively little work has been done to explicitly address the strong heterogeneity present in many processes involving human behavior. To address this gap in the literature, I will present the multi-VAR framework, a recently developed approach for modeling heterogeneous time-series data arising from multiple individuals. I will discuss multi-VAR’s implementation in the multivar R package, the performance of the proposed approach, limitations and some recent extensions.
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Fisher, Z. F., Kim, Y., Fredrickson, B. L., & Pipiras, V. (2022). Penalized estimation and forecasting of multiple subject intensive longitudinal data. Psychometrika. https://doi.org/10.1007/s11336-021-09825-7