Drop‐out is a prevalent complication in the analysis of data from longitudinal studies, and
remains an active area of research for statisticians and other quantitative methodologists …

This article review methodologies used for analyzing ordered categorical (ordinal) response
variables. We begin by surveying models for data with a single ordinal response variable …

Missing data affect nearly every discipline by complicating the statistical analysis of collected
data. But since the 1990s, there have been important developments in the statistical …

Throughout the social, medical and other sciences the importance of understanding
complex hierarchical data structures is well understood. Multilevel modelling is now the …

We consider the problem of comparing complex hierarchical models in which the number of
parameters is not clearly defined. Using an information theoretic argument we derive a …

The first edition of Analysis for Longitudinal Data has become a classic. Describing the
statistical models and methods for the analysis of longitudinal data, it covers both the …

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 …

Hidden Markov models—most often abbreviated to the acronym “HMMs”—are one of the
most successful statistical modelling ideas that have came up in the last forty years: the use …

Since our first edition of this book, many developments in statistical mod elling based on
generalized linear models have been published, and our primary aim is to bring the book up …

INLA stands for Integrated Nested Laplace Approximations, which is a new method for fitting
a broad class of Bayesian regression models. No samples of the posterior marginal …