Multiple Systems Estimation for Sparse Capture Data: Inferential Challenges When There Are Nonoverlapping Lists. Issue 535 (3rd July 2021)

• Record Type: Journal Article
• Authors: Chan, Lax; Silverman, Bernard W.; Vincent, Kyle
• Is Part Of: Journal of the American Statistical Association. Volume 116:Issue 535(2021)
• Page Start: 1297
• Page End: 1306
• Publication Date: 2021-07-03
• Subjects: Human trafficking; Log-linear models; Mark-recapture; Model identifiability; Model selection; Modern slavery; Statistics -- Periodicals; Statistics -- Periodicals; Statistiques -- Périodiques; États-Unis -- Statistiques -- Périodiques; 519.5
• DOI: 10.1080/01621459.2019.1708748
• Languages: English
• ISSNs: 0162-1459

Abstract

Abstract: Multiple systems estimation strategies have recently been applied to quantify hard-to-reach populations, particularly when estimating the number of victims of human trafficking and modern slavery. In such contexts, it is not uncommon to see sparse or even no overlap between some of the lists on which the estimates are based. These create difficulties in model fitting and selection, and we develop inference procedures to address these challenges. The approach is based on Poisson log-linear regression modeling. Issues investigated in detail include taking proper account of data sparsity in the estimation procedure, as well as the existence and identifiability of maximum likelihood estimates. A stepwise method for choosing the most suitable parameters is developed, together with a bootstrap approach to finding confidence intervals for the total population size. We apply the strategy to two empirical datasets of trafficking in US regions, and find that the approach results in stable, reasonable estimates. An accompanying R software implementation has been made publicly available. Supplementary materials for this article are available online.