English(EN) Regularized Small Area Estimation with Graph Laplacian Benchmarking priors

新的贝叶斯框架利用图拉普拉斯先验增强小区域估计

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

研究人员开发了一种新的贝叶斯小区域估计框架，该框架联合解决了跨区域信息借用和将估计值与可靠汇总数据进行基准测试的问题。该框架利用了一种新颖的基准先验家族，该家族源自一个受基准约束的正则化问题。提出的模型包括一个强制执行约束而无需额外跨区域正则化的基准先验，以及基于外部协变量信息通过图拉普拉斯引入正则化的单视图和多视图拉普拉斯基准先验。为这些退化先验的后验计算定制了马尔可夫链蒙特卡洛（MCMC）算法。通过基于数据的模拟评估了该框架，并将其应用于估计2025年哥伦比亚市政当局的平均家庭规模，结果表明基于图的正则化，特别是多视图模型，提高了估计精度。 AI

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arXiv stat.ML TIER_1 English(EN) · Jairo F\'uquene-Pati\~no, Brenda Betancourt · 2026-10-09 04:00

带图拉普拉斯正则化小区域估计基准先验

arXiv:2610.11266v1 Announce Type: cross Abstract: Small area estimation (SAE) often requires both borrowing information across areas and benchmarking estimates to reliable aggregates. We develop a Bayesian framework that addresses these two objectives jointly through a new family…

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带图拉普拉斯正则化小区域估计基准先验

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