02. Bayesian Tree Model for Binary and Categorical Data under Informative Sampling

Tree models are highly effective for analyzing survey data because they can manage numerous variables and the complex interactions often present in such datasets. Unlike their frequentist counterparts, Bayesian tree-based models naturally provided a measure of uncertainty to the model estimates produced. However, until recently, Bayesian design-consistent tree models that handle binary and categorical response data collected from complex sample designs, were not available. While several Bayesian tree modeling approaches have been developed for independent data, tree-based algorithms that account for the informative sample design for survey data remain lacking. Leveraging the flexibility of the Bayesian framework, we propose to extend the current research on Bayesian tree algorithms and develop tree-based models that effectively handle binary and categorical responses from survey data under informative sampling. We demonstrate our proposed models under a simulated setup, on the Consumer Expenditure Survey data and the American Community Survey data.

Bayesian nonparametric

CART models

Informative sample design

Categorical responses 

Mid-Level

Knowledge

Women in Statistics and Data Science 2025