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

Probability distributions are the building blocks of statistical modeling and inference. It is
therefore of the utmost importance to know which distribution to use in what circumstances …

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 …

New cylindrical distributions are proposed by combining the sine-skewed von Mises
distribution (circular part) with the Weibull distribution (linear part). This new model, the …

Motivated by segmentation issues in studies of sea current circulation, we describe a hidden
Markov random field for the analysis of spatial cylindrical data, ie bivariate spatial series of …

In this paper, we will present a new flexible distribution for three-dimensional angular data,
or data on the three-dimensional torus. Our trivariate wrapped Cauchy copula has the …

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 …

The score‐driven approach to time series modelling is able to handle circular data and
switching regimes with intra‐regime dynamics. Furthermore it enables a dynamic model to …

ABSTRACT A new hidden Markov random field model is proposed for the analysis of
cylindrical spatial series, ie bivariate spatial series of intensities and angles. It allows us to …

A hidden Markov model is proposed for segmenting cylindrical time series according to a
finite number of latent classes, associated with copula-based cylindrical densities. It …