Statistical mechanics is the science of predicting the observable properties of a multiple bodied system by studying the statistics of the behaviour of its individual constituents, whether they are atoms, molecules, photons, etc. Throughout the social, medical and other sciences the importance of understanding complex hierarchical data structures is well understood. It provides the link between macroscopic and microscopic states, and as such has the potential to be one of the most satisfying parts of an undergraduate science course - linking in an elegant manner the quantum world with everyday observations of systems containing large numbers of particles. Multilevel modelling is now the accepted statistical technique for handling such data and is widely available in computer software packages.
A thorough understanding of these techniques is therefore important for all those working in these areas.

This new edition of Multilevel Statistical Models brings these techniques together, starting from basic ideas and illustrating how more complex models are derived.
We carefully selected the subjects of the exercises to cover all the material which is most needed  and which is treated in the most well known texts on these subjects. Bayesian methodology using MCMC has been extended along with new material on smoothing models, multivariate responses, missing data, latent normal transformations for discrete responses, structural equation modeling and survival models.
Each exercise is carefully solved in full details, explaining the theory behind the solution with particular care for those issues that, from our experience, are found most difficult from the average student.
Key Features: * Provides a clear introduction and a comprehensive account of multilevel models.

Indeed, several exercises are designed to throw light on  aspects of the theory that, for one reason or another, are usually neglected with the result to make the students feel uneasy about them. This book is suitable as a comprehensive text for postgraduate courses, as well as a general reference guide.
Applied statisticians in the social sciences, economics, biological and medical disciplines will find this book beneficial.

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