Flexible domain prediction using mixed effects random forests

Faculty/Professorship: Statistics and Econometrics 
Author(s): Schmid, Timo ; Krennmair, Patrick
Title of the Journal: Journal of the Royal Statistical Society. Series C, Applied statistics
ISSN: 1467-9876, 0035-9254
Publisher Information: London : Wiley-Blackwell
Year of publication: 2022
Volume: 71
Issue: 5
Pages: 1865-1894
Language(s): English
DOI: 10.1111/rssc.12600
This paper promotes the use of random forests as versatile tools for estimating spatially disaggregated indicators in the presence of small area-specific sample sizes. Small area estimators are predominantly conceptualised within the regression-setting and rely on linear mixed models to account for the hierarchical structure of the survey data. In contrast, machine learning methods offer non-linear and non-parametric alternatives, combining excellent predictive performance and a reduced risk of model-misspecification. Mixed effects random forests combine advantages of regression forests with the ability to model hierarchical dependencies. This paper provides a coherent framework based on mixed effects random forests for estimating small area averages and proposes a non-parametric bootstrap estimator for assessing the uncertainty of the estimates. We illustrate advantages of our proposed methodology using Mexican income-data from the state Nuevo León. Finally, the methodology is evaluated in model-based and design-based simulations comparing the proposed methodology to traditional regression-based approaches for estimating small area averages.
GND Keywords: Waldgebiet; Amtliche Statistik; Nichtlineare Schätzung; Methode der partiellen kleinsten Quadrate; Regressionsanalyse
Keywords: mean squared error, official statistics, small area estimation, tree-based methods
DDC Classification: 330 Economics  
RVK Classification: QH 250   
International Distribution: Ja
Type: Article
URI: https://fis.uni-bamberg.de/handle/uniba/57459
Release Date: 23. December 2022
Project: Open-Access-Publikationskosten 2022 - 2024