Lax Chan
Multiple Systems Estimation for Sparse Capture Data: Inferential Challenges When There Are Nonoverlapping Lists
Chan, Lax; Silverman, Bernard W.; Vincent, Kyle
Authors
Bernard W. Silverman
Kyle Vincent
Abstract
© 2020 American Statistical Association. 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.
Citation
Chan, L., Silverman, B. W., & Vincent, K. (2021). Multiple Systems Estimation for Sparse Capture Data: Inferential Challenges When There Are Nonoverlapping Lists. Journal of the American Statistical Association, 116(535), 1297-1306. https://doi.org/10.1080/01621459.2019.1708748
Journal Article Type | Article |
---|---|
Acceptance Date | Dec 19, 2019 |
Online Publication Date | Feb 18, 2020 |
Publication Date | 2021 |
Deposit Date | Jan 9, 2020 |
Publicly Available Date | Mar 29, 2024 |
Journal | Journal of the American Statistical Association |
Print ISSN | 0162-1459 |
Electronic ISSN | 1537-274X |
Publisher | Taylor & Francis Open |
Peer Reviewed | Peer Reviewed |
Volume | 116 |
Issue | 535 |
Pages | 1297-1306 |
DOI | https://doi.org/10.1080/01621459.2019.1708748 |
Keywords | Statistics, Probability and Uncertainty; Statistics and Probability |
Public URL | https://nottingham-repository.worktribe.com/output/3695992 |
Publisher URL | https://www.tandfonline.com/doi/full/10.1080/01621459.2019.1708748 |
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Multiple Systems Estimation for Sparse Capture Data: Inferential Challenges when there are Non-Overlapping Lists
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Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
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