10 citations · 22 across the 18 of their papers we have counts for
19 papers
Prediction-Powered Smoothing and Validation for Disaggregated AI Evaluation
Sho Kawano, Zehang Richard Li, Paul A. Parker
Evaluating an AI system requires disaggregated assessment, as performance varies across domains such as benchmark task types or conversation types in deployed agents. Exhaustive te…
Nonprobability Samples for Small Area Estimation: A Review and Comparative Simulation Study
Sho Kawano, Daniel Vedensky, Qianyu Dong +5
Nonprobability samples (NPS) are attractive because they are less costly to collect, can provide substantially larger sample sizes, and may reach populations that traditional proba…
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…
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…
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…
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,…
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…
Topic Modeling for Free-Response Text Data from a Complex Survey
Namitha V. Pais, Scott H. Holan, Paul A. Parker
Topic Modeling is a popular statistical tool commonly used on textual data to identify the hidden thematic structure in a document collection based on the distribution of words. Ad…
The Link Between Health Insurance Coverage and Citizenship Among Immigrants: Bayesian Unit-Level Regression Modeling of Categorical Survey Data Observed with Measurement Error
Paul A. Parker, Scott H. Holan, James D. Bachmeier +1
Social scientists are interested in studying the impact that citizenship status has on health insurance coverage among immigrants in the United States. This can be done using data…
Bayesian Unit-level Models for Longitudinal Survey Data under Informative Sampling: An Analysis of Expected Job Loss Using the Household Pulse Survey
Daniel Vedensky, Paul A. Parker, Scott H. Holan
The Household Pulse Survey (HPS), recently released by the U.S. Census Bureau, gathers timely information about the societal and economic impacts of coronavirus. The first phase of…
Conjugate Modeling Approaches for Small Area Estimation with Heteroscedastic Structure
Paul A. Parker, Scott H. Holan, Ryan Janicki
Small area estimation has become an important tool in official statistics, used to construct estimates of population quantities for domains with small sample sizes. Typical area-le…
A Look into the Problem of Preferential Sampling from the Lens of Survey Statistics
Daniel Vedensky, Paul A. Parker, Scott H. Holan
An evolving problem in the field of spatial and ecological statistics is that of preferential sampling, where biases may be present due to a relationship between sample data locati…
A Bayesian Functional Data Model for Surveys Collected under Informative Sampling with Application to Mortality Estimation using NHANES
Paul A. Parker, Scott H. Holan
Functional data are often extremely high-dimensional and exhibit strong dependence structures but can often prove valuable for both prediction and inference. The literature on func…
A General Bayesian Model for Heteroskedastic Data with Fully Conjugate Full-Conditional Distributions
Paul A. Parker, Scott H. Holan, Skye A. Wills
Models for heteroskedastic data are relevant in a wide variety of applications ranging from financial time series to environmental statistics. However, the topic of modeling the va…
Computationally Efficient Deep Bayesian Unit-Level Modeling of Survey Data under Informative Sampling for Small Area Estimation
Paul A. Parker, Scott H. Holan
The topic of deep learning has seen a surge of interest in recent years both within and outside of the field of Statistics. Deep models leverage both nonlinearity and interaction e…
Computationally Efficient Bayesian Unit-Level Models for Non-Gaussian Data Under Informative Sampling
Paul A. Parker, Scott H. Holan, Ryan Janicki
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 pop…
Nonlinear Time Series Classification Using Bispectrum-based Deep Convolutional Neural Networks
Paul A. Parker, Scott H. Holan, Nalini Ravishanker
Time series classification using novel techniques has experienced a recent resurgence and growing interest from statisticians, subject-domain scientists, and decision makers in bus…
Conjugate Bayesian Unit-level Modeling of Count Data Under Informative Sampling Designs
Paul A. Parker, Scott H. Holan, Ryan Janicki
Unit-level models for survey data offer many advantages over their area-level counterparts, such as potential for more precise estimates and a natural benchmarking property. Howeve…
Unit Level Modeling of Survey Data for Small Area Estimation Under Informative Sampling: A Comprehensive Overview with Extensions
Paul A. Parker, Ryan Janicki, Scott H. Holan
Model-based small area estimation is frequently used in conjunction with survey data in order to establish estimates for under-sampled or unsampled geographies. These models can be…