A0468
Title: Twofold nested error regression models with data-driven transformation
Authors: Rachael Katwa Kyalo - Otto-Friedrich-Universitaet Bamberg (Germany) [presenting]
Timo Schmid - Otto-Friedrich-Universitaet Bamberg (Germany)
Nora Wuerz - Otto-Friedrich-Universitaet Bamberg (Germany)
Abstract: Small area estimation effectively addresses the issue of small sample sizes within subpopulations. Typically, the target population is divided into multiple nested hierarchical levels, such as counties and sub-counties. A twofold nested error regression model with random effects captures the variability across these levels. For estimating non-linear indicators like poverty measures, the twofold EBP model can be used, which relies on normality assumptions of the error terms - a condition often unmet in real data applications. The twofold nested error regression model is enhanced by incorporating a data-driven transformation, improving the model's robustness and flexibility. MSE estimation is performed using resampling methods. Model-based simulations compare the proposed model's performance with onefold EBP methods that include either area or sub-area effects. Results show that the proposed twofold EBP method adapts to the distribution shape, providing more efficient estimates than a fixed logarithmic transformation or no transformation. Finally, the twofold EBP with data-driven transformation is used to generate poverty estimates for rural and urban regions within Kenyan counties, offering a more nuanced and accurate assessment of poverty levels.
