8 papers
Local asymptotic normality for spectral-tail likelihoods under fixed-domain sampling
Alexandre B. Simas, Jonas Wallin
We establish local asymptotic normality (LAN) for joint inference on scale and roughness in stationary Gaussian random fields observed on increasingly dense regular grids in a fixe…
A bridge representation of Gaussian Whittle-Matérn fields on compact metric graphs
David Bolin, Alexandre B. Simas, Jonas Wallin
Gaussian Whittle-Matérn fields form a flexible class of Gaussian processes on compact metric graphs, where spatial dependence is governed by the geometry and connectivity of the ne…
Gaussian Processes on Directed Metric Graphs
David Bolin, Alexandre de Bustamante Simas, Erik Karlsson Strandh +1
We introduce a statistical framework for Gaussian fields indexed at arbitrary edge locations on general compact directed metric graphs. The construction is based on a stochastic di…
Hierarchical Bayesian Estimation of Covariance Matrices
Daniel Xiang, Malgorzata Bogdan, Jonas Wallin +1
We develop a hierarchical Bayesian framework for covariance matrix estimation built on a key observation: while equivariance under the full general linear group GL(p) is well known…
Adaptive Riemannian Manifold Hamiltonian Monte Carlo with Hierarchical Metric
Miika Kailas, Matti Vihola, Jonas Wallin
Hamiltonian Monte Carlo (HMC) and its dynamic extensions, such as the No-U-Turn Sampler (NUTS), are powerful Markov chain Monte Carlo methods for sampling from complex, high-dimens…
A Unified and Computationally Efficient Non-Gaussian Statistical Modeling Framework
David Bolin, Xiaotian Jin, Alexandre B. Simas +1
Datasets that exhibit non-Gaussian characteristics are common in many fields, while the current modeling framework and available software for non-Gaussian models is limited. We int…
Geometric ergodicity of Gibbs samplers for linear latent models with GIG variance mixtures
Elsiddig Awadelkarim, David Bolin, Xiaotian Jin +2
We study geometric ergodicity of the Gibbs sampler for linear latent non-Gaussian models (LLnGMs), a class of hierarchical models in which conditional Gaussian structure is preserv…
Incorporating Correlated Nugget Effects in Multivariate Spatial Models: An Application to Argo Ocean Data
Damilya Saduakhas, David Bolin, Xiaotian Jin +2
Accurate analysis of global oceanographic data, such as temperature and salinity profiles from the Argo program, requires geostatistical models capable of capturing complex spatial…