Cluster analysis finds groups in data automatically. Most methods have been heuristic and
leave open such central questions as: how many clusters are there? Which method should I …

The variance-gamma (VG) distributions form a four-parameter family which includes as
special and limiting cases the normal, gamma and Laplace distributions. Some of the …

We propose a new data-augmentation strategy for fully Bayesian inference in models with
binomial likelihoods. The approach appeals to a new class of Pólya–Gamma distributions …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics-a
very active area of research in which few up-to-date reference works are available. This is …

" This is a great overview of the field of model-based clustering and classification by one of
its leading developers. McNicholas provides a resource that I am certain will be used by …

The benchmark theory of mathematical finance is the Black–Scholes–Merton theory, based
on Brownian motion as the driving noise process for asset prices. Here the distributions of …

This book presents statistical methods for analysis of the duration of events. The primary
focus is on models for single-spell data, events in which individual agents are observed for a …

Complex elliptically symmetric (CES) distributions have been widely used in various
engineering applications for which non-Gaussian models are needed. In this overview …

The normal inverse Gaussian distribution is defined as a variance‐mean mixture of a normal
distribution with the inverse Gaussian as the mixing distribution. The distribution determines …