Artikel ; Online: Multiple Systems Estimation for Sparse Capture Data: Inferential Challenges When There Are Nonoverlapping Lists
Journal of the American Statistical Association. 2021 July 3, v. 116, no. 535 p.1297-1306
2021
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 ... ...
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. |
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Schlagwörter | computer software ; data collection ; humans ; population size ; statistical analysis ; Human trafficking; Log-linear models; Mark-recapture; Model identifiability; Model selection; Modern slavery |
Sprache | Englisch |
Erscheinungsverlauf | 2021-0703 |
Umfang | p. 1297-1306. |
Erscheinungsort | Taylor & Francis |
Dokumenttyp | Artikel ; Online |
ZDB-ID | 2064981-2 |
ISSN | 1537-274X |
ISSN | 1537-274X |
DOI | 10.1080/01621459.2019.1708748 |
Datenquelle | NAL Katalog (AGRICOLA) |
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