• DOI:10.1111/J.1541-0420.2006.00565.X
• Corpus ID: 6194737

Equivalence of truncated count mixture distributions and mixtures of truncated count distributions.

@article{Bhning2006EquivalenceOT,
  title={Equivalence of truncated count mixture distributions and mixtures of truncated count distributions.},
  author={Dankmar B{\"o}hning and Ronny Kuhnert},
  journal={Biometrics},
  year={2006},
  volume={62 4},
  pages={
          1207-15
        }
}

• D. Böhning, R. Kuhnert
• Published 1 December 2006
• Mathematics
• Biometrics

This article is about modeling count data with zero truncation. A parametric count density family is considered. The truncated mixture of densities from this family is different from the mixture of truncated densities from the same family. Whereas the former model is more natural to formulate and to interpret, the latter model is theoretically easier to treat. It is shown that for any mixing distribution leading to a truncated mixture, a (usually different) mixing distribution can be found so that the associated mixture of truncated densities equals the truncated mixture, and vice versa. This implies that the likelihood surfaces for both situations agree, and in this sense both models are equivalent. Zero-truncated count data models are used frequently in the capture-recapture setting to estimate population size, and it can be shown that the two Horvitz-Thompson estimators, associated with the two models, agree. In particular, it is possible to achieve strong results for mixtures of truncated Poisson densities, including reliable, global construction of the unique NPMLE (nonparametric maximum likelihood estimator) of the mixing distribution, implying a unique estimator for the population size. The benefit of these results lies in the fact that it is valid to work with the mixture of truncated count densities, which is less appealing for the practitioner but theoretically easier. Mixtures of truncated count densities form a convex linear model, for which a developed theory exists, including global maximum likelihood theory as well as algorithmic approaches. Once the problem has been solved in this class, it might readily be transformed back to the original problem by means of an explicitly given mapping. Applications of these ideas are given, particularly in the case of the truncated Poisson family.