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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2506.21023 |
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| _version_ | 1866913913583435776 |
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| author | Flynn, Mallory J Gustafson, Paul |
| author_facet | Flynn, Mallory J Gustafson, Paul |
| contents | The weighted multiplier method (WMM) is an extension of the traditional method of back-calculation method to estimate the size of a target population, which synthesizes available evidence from multiple subgroups of the target population with known counts and estimated proportions by leveraging the tree-structure inherent to the data. Hierarchical Bayesian models offer an alternative to modeling population size estimation on such a structure, but require non-trivial theoretical and practical knowledge to implement. While the theory underlying the WMM methodology may be more accessible to researchers in diverse fields, a barrier still exists in execution of this method, which requires significant computation. We develop two \texttt{R} packages to help facilitate population size estimation on trees using both the WMM and hierarchical Bayesian modeling; \textit{AutoWMM} simplifies WMM estimation for any general tree topology, and \textit{JAGStree} automates the creation of suitable JAGS MCMC modeling code for these same networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_21023 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AutoWMM and JAGStree -- R packages for Population Size Estimation on Relational Tree-Structured Data Flynn, Mallory J Gustafson, Paul Computation The weighted multiplier method (WMM) is an extension of the traditional method of back-calculation method to estimate the size of a target population, which synthesizes available evidence from multiple subgroups of the target population with known counts and estimated proportions by leveraging the tree-structure inherent to the data. Hierarchical Bayesian models offer an alternative to modeling population size estimation on such a structure, but require non-trivial theoretical and practical knowledge to implement. While the theory underlying the WMM methodology may be more accessible to researchers in diverse fields, a barrier still exists in execution of this method, which requires significant computation. We develop two \texttt{R} packages to help facilitate population size estimation on trees using both the WMM and hierarchical Bayesian modeling; \textit{AutoWMM} simplifies WMM estimation for any general tree topology, and \textit{JAGStree} automates the creation of suitable JAGS MCMC modeling code for these same networks. |
| title | AutoWMM and JAGStree -- R packages for Population Size Estimation on Relational Tree-Structured Data |
| topic | Computation |
| url | https://arxiv.org/abs/2506.21023 |