55 citations · 113 across the 11 of their papers we have counts for
11 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…
Increasing trust in new data sources: crowdsourcing image classification for ecology
Edgar Santos-Fernandez, Julie Vercelloni, Aiden Price +4
Crowdsourcing methods facilitate the production of scientific information by non-experts. This form of citizen science (CS) is becoming a key source of complementary data in many f…
Bayesian Design with Sampling Windows for Complex Spatial Processes
Katie Buchhorn, Kerrie Mengersen, Edgar Santos-Fernandez +2
Optimal design facilitates intelligent data collection. In this paper, we introduce a fully Bayesian design approach for spatial processes with complex covariance structures, like…
SSNbayes: An R package for Bayesian spatio-temporal modelling on stream networks
Edgar Santos-Fernandez, Jay M. Ver Hoef, James M. McGree +3
Spatio-temporal models are widely used in many research areas from ecology to epidemiology. However, most covariance functions describe spatial relationships based on Euclidean dis…
Understanding links between water-quality variables and nitrate concentration in freshwater streams using high-frequency sensor data
Claire Kermorvant, Benoit Liquet, Guy Litt +5
Real time monitoring using in situ sensors is becoming a common approach for measuring water quality within watersheds. High frequency measurements produce big data sets that prese…
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…