Statistics Colloquium: Paul A. Parker, University of California Santa Cruz

This event is part of the Spring 2022 Statistics Colloquium

Computationally Efficient Bayesian Unit-Level Modeling of Non-Gaussian Survey Data under Informative Sampling

Presented by Paul A. Parker, Assistant Professor, Department of Statistics, University of California Santa Cruz

Wednesday, March 2, 2022
4:00 p.m. ET

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 multi-scale, 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.

Speaker Bio

Dr. Parker is currently an assistant professor in the Department of Statistics at the University of California, Santa Cruz. He obtained his Ph.D. in Statistics at the University of Missouri, where he was a recipient of the U.S. Census Bureau Dissertation Fellowship, and a recipient of the University of Missouri Population, Education and Health Center Interdisciplinary Doctoral Fellowship. His dissertation work was focused on Bayesian methods for modeling non-Gaussian unit-level survey data under informative sampling, with an emphasis on application to small area estimation. He is broadly interested in modeling dependent data (time-series, spatial, functional, etc.) for a variety of applications including official statistics, social sciences, and ecology. He is also interested in integration of modern machine learning and data science techniques to help improve statistical models.