Clustered coefficient logistic linear mixed models in small area estimation

Music City Center 

Logistic linear mixed models are used in small area estimation to construct unit level model-based estimators for binary outcomes. Instead of assuming common regression coefficients for all small domains in the traditional model, a new model with clustered coefficients is proposed with consideration of random effects, which allows different regression coefficients or intercepts in different clusters of domains. To achieve the goal, an optimization problem based on penalized quasi-likelihood (PQL) and pairwise penalties is considered. A new algorithm based on the linearized alternating direction method of multipliers (ADMM) algorithm is developed to find clusters and estimate parameters simultaneously. Simulations are used to compare the proposed approach and traditional approaches to show the advantages of the proposed estimator.

Logistic linear mixed models

Clustering

ADMM algorithm

Small area estimation 

Survey Research Methods Section