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Multiple Systems Estimation for Sparse Capture Data: Inferential Challenges When There Are Nonoverlapping Lists

Chan, Lax; Silverman, Bernard W.; Vincent, Kyle

Multiple Systems Estimation for Sparse Capture Data: Inferential Challenges When There Are Nonoverlapping Lists Thumbnail


Authors

Lax Chan

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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