11 papers
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…
Echo State Networks for Spatio-Temporal Area-Level Data
Zhenhua Wang, Scott H. Holan, Christopher K. Wikle
Spatio-temporal area-level datasets play a critical role in official statistics, providing valuable insights for policy-making and regional planning. Accurate modeling and forecast…
Toward a Principled Framework for Disclosure Avoidance
Michael B Hawes, Evan M Brassell, Anthony Caruso +16
Responsible disclosure limitation is an iterative exercise in risk assessment and mitigation. From time to time, as disclosure risks grow and evolve and as data users' needs change…