Summary Continuously indexed Gaussian fields (GFs) are the most important ingredient in
spatial statistical modelling and geostatistics. The specification through the covariance …

A survey of recent developments in the theory and application of composite likelihood is
provided, building on the review paper of Varin (2008). A range of application areas …

Computer simulation experiments are essential to modern scientific discovery, whether that
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …

The Gaussian process is an indispensable tool for spatial data analysts. The onset of the
“big data” era, however, has lead to the traditional Gaussian process being computationally …

Spatial process models for analyzing geostatistical data entail computations that become
prohibitive as the number of spatial locations become large. This article develops a class of …

Gaussian processes (GPs) are commonly used as models for functions, time series, and
spatial fields, but they are computationally infeasible for large datasets. Focusing on the …

Structured additive regression models are perhaps the most commonly used class of models
in statistical applications. It includes, among others,(generalized) linear …

We provide a new approach to approximate emulation of large computer experiments. By
focusing expressly on desirable properties of the predictive equations, we derive a family of …

With scientific data available at geocoded locations, investigators are increasingly turning to
spatial process models for carrying out statistical inference. Over the last decade …

We develop a multiresolution model to predict two-dimensional spatial fields based on
irregularly spaced observations. The radial basis functions at each level of resolution are …