Title: Estimating Time-Varying Mixed Graphical Models

Author: Jonas Haslbeck

Affiliation: University of Amsterdam

Abstract:
Graphical models have become a popular way to abstract complex systems and gain
insights into relational patterns among observed variables. For temporally
evolving systems, time-varying graphical models offer additional insights as
they provide information about organizational processes, information diffusion,
vulnerabilities and the potential impact of interventions. In many of these
situations the variables of interest do not follow the same type of
distribution, for instance, one might be interested in the relations between
physiological and psychological measures (continuous) and the type of drug
(categorical) in a medical context. We present a novel method based on
generalized covariance matrices and kernel smoothed neighborhood regression to
estimate time-varying mixed graphical models in a high-dimensional setting. In
addition to our theory, we present a freely available software implementation in
R, performance benchmarks in realistic situations and an illustration of our
method using a dataset from the field of psychopathology.