9 citations · 11 across the 3 of their papers we have counts for
6 papers
Approximate Bayesian Inference via Sparse grid Quadrature Evaluation for Hierarchical Models
Joshua Hewitt, Jennifer A. Hoeting
We combine conditioning techniques with sparse grid quadrature rules to develop a computationally efficient method to approximate marginal, but not necessarily univariate, posterio…
Improved return level estimation via a weighted likelihood, latent spatial extremes model
Joshua Hewitt, Miranda J. Fix, Jennifer A. Hoeting +1
Uncertainty in return level estimates for rare events, like the intensity of large rainfall events, makes it difficult to develop strategies to mitigate related hazards, like flood…
Remote effects spatial process models for modeling teleconnections
Joshua Hewitt, Jennifer A. Hoeting, James Done +1
While most spatial data can be modeled with the assumption that distant points are uncorrelated, some problems require dependence at both far and short distances. We introduce a mo…
A Review of Nonparametric Hypothesis Tests of Isotropy Properties in Spatial Data
Zachary D. Weller, Jennifer A. Hoeting
An important aspect of modeling spatially-referenced data is appropriately specifying the covariance function of the random field. A practitioner working with spatial data is prese…
A penalized simulated maximum likelihood approach in parameter estimation for stochastic differential equations
Libo Sun, Chihoon Lee, Jennifer A. Hoeting
We consider the problem of estimating parameters of stochastic differential equations (SDEs) with discrete-time observations that are either completely or partially observed. The t…
Multilevel latent Gaussian process model for mixed discrete and continuous multivariate response data
Erin M. Schliep, Jennifer A. Hoeting
We propose a Bayesian model for mixed ordinal and continuous multivariate data to evaluate a latent spatial Gaussian process. Our proposed model can be used in many contexts where…