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| Format: | Preprint |
| Published: |
2026
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| Online Access: | https://arxiv.org/abs/2605.26607 |
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| _version_ | 1866913162621616128 |
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| author | Lu, Zhiyuan |
| author_facet | Lu, Zhiyuan |
| contents | The use of dual system estimation (DSE) is heavily used in Census Bureau operations. With DSE methods, it is important to implement methods to infer the population size among those with missing data from one or both data sources. The use of log-linear models, calculated through EM algorithms, promises a way for estimation of counts among all groups with incomplete recorded data, as displayed by Van der Heijden et al. 2022. Unfortunately, the numerical computations involved scale very poorly the more the population is divided, to the point where simultaneous analysis of several demographic and geographic factors, such as state of residence and ethnicity, becomes computationally infeasible. Here, an alternative method to calculate the log-linear estimates will be provided, which can calculate the maximum likelihood estimator in orders of computation lower than through the EM algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_26607 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Log-linear Model for Dual System Estimation and Computational Considerations Lu, Zhiyuan Computation Applications The use of dual system estimation (DSE) is heavily used in Census Bureau operations. With DSE methods, it is important to implement methods to infer the population size among those with missing data from one or both data sources. The use of log-linear models, calculated through EM algorithms, promises a way for estimation of counts among all groups with incomplete recorded data, as displayed by Van der Heijden et al. 2022. Unfortunately, the numerical computations involved scale very poorly the more the population is divided, to the point where simultaneous analysis of several demographic and geographic factors, such as state of residence and ethnicity, becomes computationally infeasible. Here, an alternative method to calculate the log-linear estimates will be provided, which can calculate the maximum likelihood estimator in orders of computation lower than through the EM algorithm. |
| title | Log-linear Model for Dual System Estimation and Computational Considerations |
| topic | Computation Applications |
| url | https://arxiv.org/abs/2605.26607 |