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

The expectation–maximization (EM) algorithm is a familiar tool for computing the maximum
likelihood estimate of the parameters in hidden Markov and semi‐Markov models. This …

A class of multivariate linear models under the longitudinal setting, in which unobserved
heterogeneity may evolve over time, is introduced. A latent structure is considered to model …

The assessment of pollution exposure is based on the analysis of a multivariate time series
that include the concentrations of several pollutants as well as the measurements of multiple …

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 …

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 …

Motivated by studies of wildfire seasonality, we propose a nonhomogeneous hidden Markov
random field to model the spatial distribution of georeferenced fire occurrences during the …

In this work, we introduce a model for circular data analysis to robustly estimate parameters,
under a longitudinal clustering setting. A hidden Markov model for longitudinal circular data …

Statistical inference on the circle may strongly depend on the chosen reference system.
Here, we introduce necessary and sufficient conditions to avoid inferential problems and …

This article considers a circular regression model for clustered data, where both the cluster
effects and the regression errors have von Mises distributions. It involves β, a vector of …