91 citations · 257 across the 49 of their papers we have counts for
6 papers · 1 filter
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…
Bayesian Nonparametric Multivariate Spatial Mixture Mixed Effects Models with Application to American Community Survey Special Tabulations
Ryan Janicki, Andrew M. Raim, Scott H. Holan +1
Leveraging multivariate spatial dependence to improve the precision of estimates using American Community Survey data and other sample survey data has been a topic of recent intere…
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…