Tuesday, May 29, 2018 - 9:00am - 9:50am
Marisa Eisenberg (University of Michigan)
Connecting dynamic models with data to yield insights and predictive results often requires a variety of parameter estimation, identifiability, and uncertainty quantification techniques. These approaches can help to determine what is possible to estimate from a given model and data set, and help guide new data collection. Here, we will discuss different approaches to examining parameter identifiability and uncertainty, and examine how these issues affect parameter estimation and intervention predictions.
Monday, April 23, 2018 - 1:00pm - 1:30pm
Ines Wilms (Katholieke Universiteit Leuven)
The Vector AutoRegressive Moving Average (VARMA) model is fundamental
to the study of multivariate time series. However, estimation becomes challenging in
even relatively low-dimensional VARMA models. With growing interest in the simultaneous
modeling of large numbers of marginal time series, many authors have abandoned
the VARMA model in favor of the Vector AutoRegressive (VAR) model, which is seen as a
simpler alternative, both in theory and practice, in this high-dimensional context. However,
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