A growing interest in non-Gaussian time series, particularly in series comprised of
nonnegative integers (counts), is taking place in today's statistics literature. Count series …

The statistical properties of disease incidence are reviewed and used to characterize spatial
patterns of diseased entities (eg plants), satisfy assumptions of statistical analyses, and …

In this article we consider geometric ergodicity and likelihood-based inference for linear and
nonlinear Poisson autoregression. In the linear case, the conditional mean is linked linearly …

A class of generalized autoregressive moving average (GARMA) models is developed that
extends the univariate Gaussian ARMA time series model to a flexible observation-driven …

We consider a log-linear model for time series of counts. This type of model provides a
framework where both negative and positive association can be taken into account. In …

The use of Markov chain Monte Carlo (MCMC) methods for estimating hierarchical models
involves complex data structures and is often described as a revolutionary development. An …

Diagnostic checking is an important step in the modeling process. But while the literature on
diagnostic checks is quite extensive and many texts on time series modeling are available, it …

Publisher Summary Modeling discrete variate time series is the most challenging and, as
yet, least well developed of all areas of research in time series. The fact that variate values …

An intermediate-level treatment of Bayesian hierarchical models and their applications, this
book demonstrates the advantages of a Bayesian approach to data sets involving inferences …

Reduced-rank regression is a method with great potential for dimension reduction but has
found few applications in applied statistics. To address this, reduced-rank regression is …