7 citations · 8 across the 6 of their papers we have counts for
7 papers
deepspat: An R package for modeling nonstationary spatial and spatio-temporal Gaussian and extremes data through deep deformations
Quan Vu, Xuanjie Shao, Raphaël Huser +1
Nonstationarity in spatial and spatio-temporal processes is ubiquitous in environmental datasets, but is not often addressed in practice, due to a scarcity of statistical software…
A Single Index Approach to Integrated Species Distribution Modeling for Fisheries Abundance Data
Quan Vu, Francis K. C. Hui, A. H. Welsh +3
In fisheries ecology, species abundance data are often collected by multiple surveys, each with unique characteristics. This article is motivated by a dataset of Atlantic sea scall…
Random Effects Misspecification and its Consequences for Prediction in Generalized Linear Mixed Models
Quan Vu, Francis K. C. Hui, Samuel Muller +1
When fitting generalized linear mixed models (GLMMs), one important decision to make relates to the choice of the random effects distribution. As the random effects are unobserved,…
Constructing Large Nonstationary Spatio-Temporal Covariance Models via Compositional Warpings
Quan Vu, Andrew Zammit-Mangion, Stephen J. Chuter
Understanding and predicting environmental phenomena often requires the construction of spatio-temporal statistical models, which are typically Gaussian processes. A common assumpt…
Warped Gradient-Enhanced Gaussian Process Surrogate Models for Exponential Family Likelihoods with Intractable Normalizing Constants
Quan Vu, Matthew T. Moores, Andrew Zammit-Mangion
Markov chain Monte Carlo methods for exponential family models with intractable normalizing constant, such as the exchange algorithm, require simulations of the sufficient statisti…
Modeling Nonstationary and Asymmetric Multivariate Spatial Covariances via Deformations
Quan Vu, Andrew Zammit-Mangion, Noel Cressie
Multivariate spatial-statistical models are often used when modeling environmental and socio-demographic processes. The most commonly used models for multivariate spatial covarianc…
Deep Compositional Spatial Models
Andrew Zammit-Mangion, Tin Lok James Ng, Quan Vu +1
Spatial processes with nonstationary and anisotropic covariance structure are often used when modelling, analysing and predicting complex environmental phenomena. Such processes ma…