Mapping Community Level Outcomes

Many important statistics (e.g., poverty rates, health outcomes, economic indicators, etc.) are needed at local levels such as counties or neighborhoods. However, surveys rarely collect enough data in each location to estimate these quantities directly.

My research develops statistical models that combine information across space and sometimes using outside datasets to estimate reliable local statistics. These models are often called small area estimation models and are widely used in government agencies and international organizations to produce official statistics that inform policy decisions.

Students working on these projects learn how to build models that combine:

• hierarchical Bayesian modeling
• spatial statistics
• survey methodology

to study real policy problems.

Selected publications

• Parker, P.A. and Eideh, A. (2026+) BART-FH: Flexible Nonlinear Modeling for Small Area Estimation. Journal of Survey Statistics and Methodology.
• Wang, Z., Parker, P.A., and Holan, S.H. (2025) Variational Autoencoded Multivariate Spatial Fay-Herriot Models. Spatial Statistics, 70, 100929.
• Kawano, S., Parker, P.A., and Li, Z.R. (2025) Spatially Selected and Dependent Random Effects for Small Area Estimation with Application to Rent Burden. Journal of the Royal Statistical Society Series A, qnaf063.
• Parker, P.A., Holan, S.H., and Janicki, R. (2024) Conjugate Modeling Approaches for Small Area Estimation with Heteroscedastic Structure. Journal of Survey Statistics and Methodology, 12(4), 1061-1080.
• Parker, P.A. (2024) Nonlinear Fay-Herriot Models for Small Area Estimation using Random Weight Neural Networks. Journal of Official Statistics, 40(2), 317-332.
• Parker, P.A., Janicki, R., and Holan, S.H. (2023) Comparison of Unit Level Small Area Estimation Modeling Approaches for Survey Data Under Informative Sampling. Journal of Survey Statistics and Methodology, 11(4), 858-872.
• Parker, P.A., Janicki, R., and Holan, S.H. (2023) A Comprehensive Overview of Unit Level Modeling of Survey Data for Small Area Estimation Under Informative Sampling. Journal of Survey Statistics and Methodology, 11(4), 829-857.
• Parker, P.A., and Holan, S.H. (2023) Computationally Efficient Bayesian Unit-Level Random Neural Network Modeling of Survey Data under Informative Sampling for Small Area Estimation. Journal of the Royal Statistical Society Series A, 184(4), 722-737.
• Parker, P.A., Holan, S.H., and Janicki, R. (2022) Computationally Efficient Bayesian Unit-Level Models for Non-Gaussian Data Under Informative Sampling with Application to Estimation of Health Insurance Coverage. The Annals of Applied Statistics, 16(2), 887-904.
• Parker, P.A., Holan, S.H., Janicki, R. (2020) Conjugate Bayesian Unit-level Modelling of Count Data Under Informative Sampling Designs. Stat, 9(1): e267.