Flexible domain prediction using mixed effects random forests

Abstract

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 conceptualized 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.

Mapping from SIBDQ to EQ-5D-5L for patients with inflammatory bowel disease

“…Finally, we estimated our mapping algorithm using a mixed-effects regression forest (MERF). MERF consist of a random effect and a fixed part of the model, where the latter is estimated with a regression forest [ 33 , 34 ]. Regression forest is an approach from machine learning that does not require an assumption about the distribution of the dependent variable or the functional form of the relationship between outcome and explanatory variables.…”