2022/11/17 by Saikat Nandy, Nandy, Saikat, Scott H. Holan +5 · 1 citation
Decision Sciences · Economics, Econometrics and Finance · Environmental Science · #Applications (stat.AP) #Atmospheric and Environmental Gas Dynamics #Economic and Environmental Valuation #FOS: Computer and information sciences #Methodology (stat.ME) #demographic modeling and climate adaptation
paper · pdf · doi:10.48550/arxiv.2211.09797
openalex publication_date 2022/11/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We introduce Bayesian hierarchical models for predicting high-dimensional tabular survey data which can be distributed from one or multiple classes of distributions (e.g., Gaussian, Poisson, Binomial, etc.). We adopt a Bayesian implementation of a Hierarchical Generalized Transformation (HGT) model to deal with the non-conjugacy of non-Gaussian data models when estimated using a Latent Gaussian Process (LGP) model. Survey data are usually prone to a high degree of sampling error, and we use covariates that are prone to measurement error as well as those free of any such error. A classical measurement error component is defined to deal with the sampling error in the covariates. The proposed models can be high-dimensional and we employ the notion of basis function expansions to provide an effective approach to dimension reduction. The HGT component lends flexibility to our model to incorporate multi-type response datasets under a unified latent process model framework. To demonstrate the applicability of our methodology, we provide the results from simulation studies and data applications arising from a dataset consisting of the U.S. Census Bureau's American Community Survey (ACS) 5-year period estimates of the total population count under the poverty threshold and the ACS 5-year period estimates of median housing costs at the county level across multiple states in the USA.