Interest in the skew-normal and related families of distributions has grown enormously over
recent years, as theory has advanced, challenges of data have grown, and computational …

In the last two decades or so, much work has been dedicated to the portion of distribution
theory stemming from the skew-normal distribution and its ramification. This contribution …

This paper provides an introductory overview of a portion of distribution theory which is
currently under intense development. The starting point of this topic has been the so‐called …

This book reviews the state-of-the-art advances in skew-elliptical distributions and provides
many new developments in a single volume, collecting theoretical results and applications …

We introduce the sinh-arcsinh transformation and hence, by applying it to a generating
distribution with no parameters other than location and scale, usually the normal, a new …

Finite mixtures of skew distributions have emerged as an effective tool in modelling
heterogeneous data with asymmetric features. With various proposals appearing rapidly in …

In this chapter, the basic concepts of clustering are introduced. Moreover, the most relevant
decisions to be made for the practical application of clustering methods are listed and briefly …

The robustness problem is tackled by adopting a parametric class of distributions flexible
enough to match the behaviour of the observed data. In a variety of practical cases, one …

Parametric families of multivariate nonnormal distributions have received considerable
attention in the past few decades. The authors propose a new definition of a selection …

This paper provides a flexible mixture modeling framework using the multivariate skew
normal distribution. A feasible EM algorithm is developed for finding the maximum likelihood …