33 citations · 52 across the 5 of their papers we have counts for
5 papers
Indexing and Partitioning the Spatial Linear Model for Large Data Sets
Jay M. Ver Hoef, Michael Dumelle, Matt Higham +2
We consider four main goals when fitting spatial linear models: 1) estimating covariance parameters, 2) estimating fixed effects, 3) kriging (making point predictions), and 4) bloc…
Marginal Inference for Hierarchical Generalized Linear Mixed Models with Patterned Covariance Matrices Using the Laplace Approximation
Jay M. Ver Hoef, Eryn Blagg, Michael Dumelle +3
Using a hierarchical construction, we develop methods for a wide and flexible class of models by taking a fully parametric approach to generalized linear mixed models with complex…
Bayesian spatio-temporal models for stream networks
Edgar Santos-Fernandez, Jay M. Ver Hoef, Erin E. Peterson +3
Spatio-temporal models are widely used in many research areas including ecology. The recent proliferation of the use of in-situ sensors in streams and rivers supports space-time wa…
A Linear Mixed Model Formulation for Spatio-Temporal Random Processes with Computational Advances for the Separable and Product-Sum Covariances
Michael Dumelle, Jay M. Ver Hoef, Claudio Fuentes +1
We describe spatio-temporal random processes using linear mixed models. We show how many commonly used models can be viewed as special cases of this general framework and pay close…
Estimating Abundance from Counts in Large Data Sets of Irregularly-Spaced Plots using Spatial Basis Functions
Jay M. Ver Hoef, John K. Jansen
Monitoring plant and animal populations is an important goal for both academic research and management of natural resources. Successful management of populations often depends on o…