Mainstream statistical methodology is generally applicable to data observed in Euclidean
space. There are, however, numerous contexts of considerable scientific interest in which …

The interpretation of the effect of predictors in projected normal regression models is not
straight‐forward. The main aim of this paper is to make this interpretation easier such that …

Supplementary Material to the paper is provided in Masci, Ieva and Paganoni (2022) and
contains the following three sections. SM-A: Proof of the increasing likelihood property of the …

Circular data can be found across many areas of science, for instance meteorology (eg,
wind directions), ecology (eg, animal movement directions), or medicine (eg, seasonality in …

This paper introduces a multivariate circular–linear (or poly-cylindrical) distribution obtained
by combining the projected and the skew-normal. We show the flexibility of our proposal, its …

Understanding when floods occur is fundamental to reducing flood risk, yet depictions of
flood seasonality rarely address two key issues: multiple seasons and the periodicity of …

CircSpaceTime is the only R package, currently available, that implements Bayesian models
for spatial and spatio-temporal interpolation of circular data. Such data are often found in …

This article gives a comprehensive theoretical framework to the modeling, inference, and
applications of Gaussian random fields using what we term the hypertorus as an index set …

In various environmental studies, geoscience variables not only have the characteristics of
time and space, but also are influenced by other variables. Multivariate spatiotemporal …

The paper deals with multivariate Gaussian random fields defined over generalized product
spaces that involve the hypertorus. The assumption of Gaussianity implies the finite …