Abstract The Expectation Maximization (EM) algorithm has been widely used for parameter
estimation in data-driven process identification. EM is an algorithm for maximum likelihood …

Abstract Analysis of count data is required in many areas of biometric interest. Often the
simple Poisson distribution is not appropriate, since an extra‐number of zero counts occur in …

" Introduction God made the integers, all the rest is the work of man.-Kronecker. This book is
concerned with models of event counts. An event count refers to the number of times an …

The only single-source——now completely updated and revised——to offer a unified
treatment of the theory, methodology, and applications of the EM algorithm Complete with …

" 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 …

I have many persons to thank for their part in the writing of this monograph. First and
foremost, John Grego was responsible for the successful proposal that led to the lecture …

Drawing on the authors' extensive research in the analysis of categorical longitudinal data,
this book focuses on the formulation of latent Markov models and the practical use of these …

In this article we develop a finite mixture negative binomial count model that accommodates
unobserved heterogeneity in an intuitive and analytically tractable manner. This model, the …

Finite mixture models have a long history in statistics, having been used to model population
heterogeneity, generalize distributional assumptions, and lately, for providing a convenient …

Parsimonious Gaussian mixture models are developed using a latent Gaussian model
which is closely related to the factor analysis model. These models provide a unified …