Engineering problems often involve data sources of variable fidelity with different costs of
obtaining an observation. In particular, one can use both a cheap low fidelity function (eg a …

Bayesian fixed-domain asymptotics for covariance parameters in a Gaussian process model
Page 1 The Annals of Statistics 2022, Vol. 50, No. 6, 3334–3363 https://doi.org/10.1214/22-AOS2230 …

As the spatial features of multivariate data are increasingly central in researchers' applied
problems, there is a growing demand for novel spatially-aware methods that are flexible …

This dissertation explores parameter estimation for Gaussian random fields and multivariate
Gaussian random processes under fixed-domain asymptotics, a crucial framework for …

Engineering problems often involve data sources of variable fidelity with different costs of
obtaining an observation. In particular, one can use both a cheap low fidelity function (eg a …

This dissertation addresses the asymptotic theory behind parametric estimation in spatial
regression models. In spatial statistics, there are two prominent types of asymptotic …

Bivariate spatial models are used to represent the joint relationship between two processes
observed over space. By modeling two processes jointly, we may be able to enhance …

Assessing the significance of the correlation between the components of a bivariate random
field is of great interest in the analysis of spatial-spatial data. In this chapter, testing the …

Аннотация Для решения задачи регрессии в случае наличия данных разной точности
часто используется кокригинг—обобщение регрессии на основе гауссовских процессов …