Artificial intelligence and machine learning (AI–ML) offer vast potential in optimal design,
monitoring, and control of biopharmaceutical manufacturing. The driving forces for adoption …

Meta-analysis in the presence of unexplained heterogeneity is frequently undertaken by
using a random-effects model, in which the effects underlying different studies are assumed …

" 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 …

Multilevel analysis is a methodology for the analysis of data with complex patterns of
variability, with a focus on nested sources of such variability–pupils in classes, employees in …

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 …

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 …

Two convergence aspects of the EM algorithm are studied:(i) does the EM algorithm find a
local maximum or a stationary value of the (incomplete-data) likelihood function?(ii) does the …

The availability of powerful computing methods in recent decades has thrust linear and
nonlinear mixed models into the mainstream of statistical application. This volume offers a …

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 …

Maximum likelihood estimation of item parameters in the marginal distribution, integrating
over the distribution of ability, becomes practical when computing procedures based on an …