7 papers
Irregularly and incompletely sampled random fields in the Earth sciences: Analysis and synthesis of parameterized covariance models
Olivia L. Walbert, Frederik J. Simons, Arthur P. Guillaumin +1
We study how sampling geometry contributes to uncertainty in modeling spatial geophysical observations as sampled random fields characterized by stationary, isotropic, parametric c…
Maximum-likelihood estimation of the Matérn covariance structure of isotropic spatial random fields on finite, sampled grids
Frederik J. Simons, Olivia L. Walbert, Arthur P. Guillaumin +3
We present a statistically and computationally efficient spectral-domain maximum-likelihood procedure to solve for the structure of Gaussian spatial random fields within the Matern…
Calibrated Bayesian inference for random fields on large irregular domains using the debiased spatial Whittle likelihood
Thomas Goodwin, Arthur Guillaumin, Matias Quiroz +2
Bayesian inference for stationary random fields is computationally demanding. Whittle-type likelihoods in the frequency domain based on the fast Fourier Transform (FFT) have severa…
Tukey g-and-h neural network regression for non-Gaussian data
Arthur P. Guillaumin, Natalia Efremova
This paper addresses non-Gaussian regression with neural networks via the use of the Tukey g-and-h distribution.The Tukey g-and-h transform is a flexible parametric transform with…
The Debiased Spatial Whittle Likelihood
Arthur P. Guillaumin, Adam M. Sykulski, Sofia C. Olhede +1
We provide a computationally and statistically efficient method for estimating the parameters of a stochastic covariance model observed on a regular spatial grid in any number of d…
Analysis of nonstationary modulated time series with applications to oceanographic flow measurements
Arthur P. Guillaumin, Adam M. Sykulski, Sofia C. Olhede +2
We propose a new class of univariate nonstationary time series models, using the framework of modulated time series, which is appropriate for the analysis of rapidly-evolving time…
The De-Biased Whittle Likelihood
Adam M. Sykulski, Sofia C. Olhede, Arthur P. Guillaumin +2
The Whittle likelihood is a widely used and computationally efficient pseudo-likelihood. However, it is known to produce biased parameter estimates for large classes of models. We…