This article considers critically how one of the oldest and most widely applied statistical
methods, principal components analysis (PCA), is employed with spatial data. We first …

Spatial statistics is concerned with the analysis of data that have spatial locations associated
with them, and those locations are used to model statistical dependence between the data …

Focusing on Bayesian approaches and computations using simulation-based methods for
inference, Time Series: Modeling, Computation, and Inference integrates mainstream …

For most problems in science and engineering we can obtain data sets that describe the
observed system from variousperspectives and record the behavior of its individual …

Generalized linear latent variable models (GLLVMs) have become mainstream models in
this analysis of correlated, m‐dimensional data. GLLVMs can be seen as a reduced‐rank …

In geostatistics, it is common to model spatially distributed phenomena through an
underlying stationary and isotropic spatial process. However, these assumptions are often …

Geographically and temporally granular housing price indexes are difficult to construct. Data
sparseness, in particular, is a limiting factor in their construction. A novel application of a …

We propose a novel class of dynamic factor models for spatiotemporal areal data. This novel
class of models assumes that the spatiotemporal process may be represented by some few …

Big spatial datasets are very common in scientific problems, such as those involving remote
sensing of the earth by satellites, climate-model output, small-area samples from national …

Non-Gaussian spatial and spatio-temporal data are becoming increasingly prevalent, and
their analysis is needed in a variety of disciplines. FRK is an R package for spatial and …