Unit-Level Modeling for Dependent Multi-type Survey Data: Using Pseudo-likelihood Approach

Music City Center 

We proposed a unit-level modeling framework for dependent multi-type survey data. Our model combines highly correlated binomial and Gaussian responses by employing a pseudo-likelihood approach that effectively incorporates varying unit weights and adapts to complex survey designs. By integrating different response types within a single model, the inherent correlations are leveraged to enhance predictive performance. For computational efficiency, Polya-Gamma data augmentation is utilized to introduce latent variables that facilitate Gibbs sampling during model estimation. This strategy simplifies the computational process while retaining the flexibility necessary to capture the nuances of both response types. Comparative analyses with traditional univariate methods, based on empirical simulation studies and real data analysis, indicate promising improvements in prediction accuracy.

Unit-level modeling; Highly dependent data

Multi-type model; pseudo-likelihood approach;

Polya-Gamma data augmentation; latent variables; Gibbs sampling