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Abstract:
Many systems with propagation dynamics, such as spike propagation in neural networks and spreading of
infectious diseases, can be approximated by autoregressive models. The estimation of model parameters can be
complicated by the experimental limitation that one observes only a fraction of the system (subsampling) and
potentially time-dependent parameters, leading to incorrect estimates. We show analytically how to overcome
the subsampling bias when estimating the propagation rate for systems with certain nonstationary external input.
This approach is readily applicable to trial-based experimental setups and seasonal fluctuations as demonstrated
on spike recordings from monkey prefrontal cortex and spreading of norovirus and measles.
