Model-Based Estimators Using Area-Level Models With Clustered Effects

Model-based estimators are widely used in small area estimation problems to provide reliable estimates for domains with small sample sizes. When auxiliary information is available only at the area level, area-level models are typically used. We propose a new estimator based on an area-level model with clustered effects. This model allows for heterogeneity in regression coefficients across areas by incorporating clustered coefficients. Pairwise penalties are used to simultaneously identify clusters and estimate parameters. In simulation studies, we compare the performance of the proposed estimator with that of existing estimators. Additionally, we apply the new estimator to the Forest Inventory and Analysis data.