AI Article Synopsis
- The one-inflated positive Poisson mixture model (OIPPMM) is designed for estimating unknown population sizes using truncated count data from capture-recapture studies.
- The model effectively addresses issues of one-inflation and unobserved heterogeneity that are common in such data.
- Compared to other estimators, the OIPPMM produces more reliable results and overcomes the boundary problem by treating boundary components as one-inflation instead.
Article Abstract
The one-inflated positive Poisson mixture model (OIPPMM) is presented, for use as the truncated count model in Horvitz-Thompson estimation of an unknown population size. The OIPPMM offers a way to address two important features of some capture-recapture data: one-inflation and unobserved heterogeneity. The OIPPMM provides markedly different results than some other popular estimators, and these other estimators can appear to be quite biased, or utterly fail due to the boundary problem, when the OIPPMM is the true data-generating process. In addition, the OIPPMM provides a solution to the boundary problem, by labelling any mixture components on the boundary instead as one-inflation.
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