Abstract
When training data points for a prediction algorithm are connected by a network, it creates dependency, which reduces effective sample size but also creates an opportunity to improve prediction by leveraging information from neighbors. Multiple prediction methods on networks taking advantage of this opportunity have been developed, but they are rarely interpretable or have uncertainty measures available. This talk will cover two contributions bridging this gap. One is a conformal prediction method for network-assisted regression. The other is a family of flexible network-assisted models built upon a generalization of random forests (RF+), which both achieves competitive prediction accuracy and can be interpreted through feature importance measures. Importantly, it allows one to separate the importance of node covariates in prediction from the importance of the network itself. These tools help broaden the scope and applicability of network-assisted prediction to practical applications.
This talk is based on joint work with Robert Lunde, Tiffany Tang, and Ji Zhu.
Presenter
Liza Levina is the Vijay Nair Collegiate Professor and Chair of Statistics and affiliated faculty at the Michigan Institute for Data Science and the Center for the Study of Complex Systems. She received her PhD in Statistics from UC Berkeley in 2002, and has been at the University of Michigan since. She is well known for her work on high-dimensional statistical inference and statistical network analysis covering a wide range of methods, theory, and applications. Her current application interests are in neuroimaging. She is a recipient of the ASA junior Noether Award, a fellow of the ASA and the IMS, a Web of Science Highly Cited Researcher, an invited speaker at the 2018 International Congress of Mathematicians, and an IMS Medallion lecturer.
