Wiesner Vos
Wiesner holds a M.Sc in mathematical statistics from the University of Stellenbosch, and a DPhil in applied statistics from the University of Oxford where he read as a South African Rhodes Scholar. He is affiliated with the Royal Statistical Society as chartered statistician. He worked in bioinformatics, online gaming, credit risk and at a paid search agency before joining Google in their London office as a statistician in 2011. He serves a a selector for the Schmidt Science Fellowship.
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Many empirical micro-economics studies rely on consumer panels. For example, TV and web metering panels track TV and online usage of individuals. Sometimes more than one panel are available although these panels use different metering technologies and are subject to varying degrees of missingness. The problem we consider here is how to combine imputation based on two panels which have similar but not identical statistical characteristics. In the US, we have two two-screen panels, panel A (TV + desktop) and panel B(desktop + mobile) which are both calibrated to the US internet population. We want to estimate a count of ad impressions across all three-screens. As desktop impressions are metered in both panels, we fit a joint imputation model by pooling observed desktop impression counts across panels. After imputation on panel B, we fit a truncated negative binomial hurdle regression of mobile impression count over desktop impression count, demographic information, etc. And then, for each panelist in the panel A, we predict his/her mobile impression counts. In this way, we 'impute' mobile impressions in the panel A to facilitate three-screens measurements.
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We present a method for measuring the reach and frequency of online ad campaigns by audience attributes. This method uses a combination of data sources, including ad server logs, publisher provided user data (PPD), census data, and a representative online panel. It adjusts for known problems with cookie data and potential non-representative and inaccurate PPD. It generalizes for multiple publishers and for targeting based on the PPD. The method includes the conversion of adjusted cookie counts to unique audience counts. The benefit of our method is that we get both reduced variance from server logs and reduced bias from the panel. Simulation results and a case study are presented.
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