7 papers · 1 filter
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…
Bayesian Modeling of Gibbs Point Processes via Basis Function Expansions
Christopher Hassett, Athanasios C. Micheas, Scott H. Holan +1
We present a hierarchical Bayesian framework for non-homogeneous pairwise interaction Gibbs point process models, where the global and local effect functions are modeled via basis…
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…
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…
A Criterion for Aggregation Error for Multivariate Spatial Data
Ranadeep Daw, Jonathan R. Bradley, Christopher K. Wikle +1
The criterion for aggregation error (CAGE) is an important metric that aims to measure errors that arise in multiscale (or multi-resolution) spatial data, referred to as the modifi…