collaborators

7 papers

stat.ME2026

Bias Reduction for Local Polynomial Derivative Estimation

Fujia Chang, W. John Braun

Local polynomial smoothing is commonly used in non-parametric regression, but local linear derivative estimation still has a bias of order . This paper proposes an iterativ…

stat.ME2026

Scalable Joint Modeling of Dependent Multi-Type Survey Data for Small Area Estimation

Zewei Kong, Paul A. Parker, Scott H. Holan

We develop a Bayesian area-level small area estimation framework that jointly models binomial and Gaussian survey responses through shared spatial random effects. This work is moti…

stat.ME2026

On Data Thinning for Model Validation in Small Area Estimation

Sho Kawano, Paul A. Parker, Zehang Richard Li

Small area estimation produces estimates of population parameters for geographic and demographic subgroups with limited sample sizes. Such estimates are critical for policy decisio…

stat.ME2026

A Bayesian Approach to Unit-level Dependent Multi-type Survey Data

Zewei Kong, Paul A. Parker, Jonathan R. Bradley +1

The American Community Survey (ACS) Public Use Microdata Sample (PUMS) provides access to a wide range of unit-level survey data consisting of correlated Gaussian and binomial dist…

stat.ML2025

Variational Autoencoded Multivariate Spatial Fay-Herriot Models

Zhenhua Wang, Paul A. Parker, Scott H. Holan

Small area estimation models are essential for estimating population characteristics in regions with limited sample sizes, thereby supporting policy decisions, demographic studies,…

stat.ME2025

Bayesian Unit-level Modeling of Categorical Survey Data with a Longitudinal Design

Daniel Vedensky, Paul A. Parker, Scott H. Holan

Categorical response data are ubiquitous in complex survey applications, yet few methods model the dependence across different outcome categories when the response is ordinal. Like…