Media-metering panels track TV and online usage of people to analyze viewing
behavior. However, panel data is often incomplete due to non-registered devices,
non-compliant panelists, or work usage. We thus propose a probabilistic model to
impute missing events in data with excess zeros using a negative-binomial hurdle
model for the unobserved events and beta-binomial sub-sampling to account for
missingness. We then use the presented models to estimate the number of people in
Germany who visit YouTube.