Key points are not available for this paper at this time.
In the field of media research, the beta binomial has performed very well for estimating the distribution of the frequency of exposures to a media vehicle. However, long-term projections have shown consistent biases. The beta binomial geometric model, an extension of the well-known beta binomial model, which incorporates non-stationarity of individuals' exposure probabilities, is able to account for these errors. In addition this beta binomial geometric framework provides insights into the sensitivity of various media statistics to non-stationarity. This model is a particular operationalization of Howard's general Dynamic Inference Model (Howard, R. A. 1965. Dynamic inference. Oper. Res. 13 (2) 712–733.). The paper focuses on applications to some television viewing and magazine readership data. The properties of the model, estimation of the parameters and statistical tests are also presented. Finally, some future research possibilities are discussed.
Sabavala et al. (Mon,) studied this question.