One-Inflation and Zero-Truncation Count Data Modelling Revisited With a View on Horvitz–Thompson Estimation of Population Size

Abstract

Estimating the size of a hard-to-count population is a challenging matter. We consider uni-list approaches in which the count of identifications per unit is the basis of analysis. Unseen units have a zero count and do not occur in the sample leading to a zero-truncated setting. Because of various mechanisms, one-inflation is often an occurring phenomena that can lead to seriously biased estimates of population size. The current work reviews some recent advances on one-inflation and zero-truncation modelling, and furthermore focuses here on the impact it has on population size estimation. The zero-truncated one-inflated and the one-inflated zero-truncated model is compared (also with the model ignoring one-inflation) in terms of Horvitz–Thompson estimation of population size. The simulation work shows clearly the biasing effect of ignoring one-inflation. Both models, the zero-truncated one-inflated and the one-inflated zero-truncated one, are suitable to model ongoing one-inflation. It is also important to choose an appropriate base-line distributional model. Finally, all models derived in the paper are illustrated on a number of case studies.