Small area learning
Small area estimation (SAE) traditionally borrows strength across domains through a prespecified working model. We introduce small area learning (SAL), a learner-agnostic framework that formulates SAE as a multi-target semi-supervised learning problem and treats outcome prediction as a nuisance task. Areas may differ through unrestricted covariate shift and structured conditional shift, with information shared through common and area-specific components. We develop standard and cross-fitted estimators for low-complexity and flexible learners, unifying synthetic, model-based, and fully residual-corrected estimation. For area means, we establish consistency and asymptotic normality and prov...
| Authors | Katarzyna Reluga, Nicola Salvati, Setareh Ranjbar et al. |
|---|---|
| Primary category | stat.ME |
| Published | 11 October 2026 |
| Last updated at source | 11 October 2026 |
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