Neuer Artikel im Journal of the Royal Statistical Society Series A

Small area estimation with generalized random forests: estimating poverty rates in Mexico

Frink, N.; Schmid, T.; Würz, N.

Abstract: The paper proposes a generalized mixed effects random forest framework for small area estimation under generalized modelling assumptions. The approach combines machine learning techniques for identifying nonlinear relationships with random effects that account for hierarchical structures in the data. It accommodates response variables from the exponential family through general link functions and develops a parametric bootstrap for mean squared error estimation. Simulation studies assess both point and uncertainty estimation and investigate the effects of converting continuous outcomes into binary variables. The methodology is applied to estimate poverty rates and reveal spatial patterns of poverty in the Mexican state of Tlaxcala.

Nicolas Frink, Timo Schmid & Nora Würz (2026): Small area estimation with generalized random forests: estimating poverty rates in Mexico, Journal of the Royal Statistical Society Series A: Statistics in Society, forthcoming, DOI: https://doi.org/10.1093/jrsssa/qnag092