1 citations · 1 across the 5 of their papers we have counts for
5 papers
Bayesian copula-based spatial random effects models for inference with complex spatial data
Alan Pearse, David Gunawan, Noel Cressie
In this article, we develop fully Bayesian, copula-based, spatial-statistical models for large, noisy, incomplete, and non-Gaussian spatial data. Our approach includes novel constr…
Power-divergence copulas: A new class of Archimedean copulas, with an insurance application
Alan R. Pearse, Howard Bondell
This paper demonstrates that, under a particular convention, the convex functions that characterise the phi divergences also generate Archimedean copulas in at least two dimensions…
Optimal prediction of positive-valued spatial processes: asymmetric power-divergence loss
Alan R. Pearse, Noel Cressie, David Gunawan
This article studies the use of asymmetric loss functions for the optimal prediction of positive-valued spatial processes. We focus on the family of power-divergence loss functions…
SSNdesign -- an R package for pseudo-Bayesian optimal and adaptive sampling designs on stream networks
Alan R. Pearse, James M. McGree, Nicholas A. Som +4
Streams and rivers are biodiverse and provide valuable ecosystem services. Maintaining these ecosystems is an important task, so organisations often monitor the status and trends i…
Monitoring through many eyes: Integrating disparate datasets to improve monitoring of the Great Barrier Reef
Erin E Peterson, Edgar Santos-Fernández, Carla Chen +13
Numerous organisations collect data in the Great Barrier Reef (GBR), but they are rarely analysed together due to different program objectives, methods, and data quality. We develo…