In this article, we provide a comprehensive review of space–time covariance functions. As
for the spatial domain, we focus on either the d‐dimensional Euclidean space or on the unit …

In recent years, interest has grown in modeling spatio-temporal data generated from
monitoring networks, satellite imaging, and climate models. Under Gaussianity, the …

Introduction to Functional Data Analysis provides a concise textbook introduction to the field.
It explains how to analyze functional data, both at exploratory and inferential levels. It also …

Modern datasets are often in the form of matrices or arrays, potentially having correlations
along each set of data indices. For example, data involving repeated measurements of …

Statistical modeling of space‐time data has often been based on separable covariance
functions, that is, covariances that can be written as a product of a purely spatial covariance …

Variables in many big-data settings are structured, arising, for example, from measurements
on a regular grid as in imaging and time series or from spatial-temporal measurements as in …

Motivated by analysis of gene expression data measured over different tissues or over time,
we consider matrix-valued random variable and matrix-normal distribution, where the …

We present Bayesian analyses of matrix-variate normal data with conditional
independencies induced by graphical model structuring of the characterizing covariance …

Recently, there has been a growing interest in the analysis of multi-dimensional data arrays
(eg when a univariate response is sampled in 3-D space or when a multivariate response is …

Separability is a common simplifying assumption on the covariance structure of
spatiotemporal functional data. We present three tests of separability, one a functional …