AI

How Many People Visit YouTube? Imputing Missing Events in Panels With Excess Zeros

Abstract

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.