# Computationally Efficient Bayesian Unit-Level Models for Non-Gaussian Data Under Informative Sampling

arXiv: https://arxiv.org/abs/2009.05642 · HTML: https://mlsanity.com/paper/2009.05642/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2020.

## Built on by (most cited first, 1 of 1)

- [Variational Approximated Restricted Maximum Likelihood Estimation for Spatial Data](https://mlsanity.com/paper/2604.07635/) (2026)

## Abstract (arXiv)

Statistical estimates from survey samples have traditionally been obtained via design-based estimators. In many cases, these estimators tend to work well for quantities such as population totals or means, but can fall short as sample sizes become small. In today's "information age," there is a strong demand for more granular estimates. To meet this demand, using a Bayesian pseudo-likelihood, we propose a computationally efficient unit-level modeling approach for non-Gaussian data collected under informative sampling designs. Specifically, we focus on binary and multinomial data. Our approach is both multivariate and multiscale, incorporating spatial dependence at the area-level. We illustrate our approach through an empirical simulation study and through a motivating application to health insurance estimates using the American Community Survey.