Search results for ""author harvey goldstein""

Throughout the social, medical and other sciences the importance of understanding complex hierarchical data structures is well understood. 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. 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. Key Features: Provides a clear introduction and a comprehensive account of multilevel models. New methodological developments and applications are explored. Written by a leading expert in the field of multilevel methodology. Illustrated throughout with real-life examples, explaining theoretical concepts. 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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Multilevel modelling facilitates the analysis of hierarchical data where observations may be nested within higher levels of classification. In health care research, for example, a study may be undertaken to determine the variability of patient outcomes where these also vary by hospital or health care region. Inference can then be made on the efficacy of health care practices. This book provides the reader with the analytical techniques required to study such data sets. * First book to focus on multilevel modelling for health and medical research * Covers the majority of analytical techniques required by health care professionals * Unifies the literature on multilevel modelling for medical and health researchers * Each contribution comes from a specialist in that area Guiding the reader through various stages, from a basic introduction through to methodological extensions and generalised linear models, this test will show how various kinds of data can be analysed in a multilevel framework. Important statistical concepts, such as sampling and outliers, are covered specifically for multilevel data. Repeated measures, outliers, institutional performance, and spatial analysis, which have great relevance to health and medical research, are all examined for multilevel models. The book is aimed at health care professionals and public health researchers interested in the application of statistics, and will also be of interest to postgraduate students studying medical statistics. Wiley Series in Probability and Statistics

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Recent books in the Wiley Series in Probability and Statistics Editors Vic Barnett J. Stuart Hunter David W. Scott Geoffrey S. Watson Ralph A. Bradley Joseph B. Kadane Adrian F.M. Smith Nicholas I. Fisher David G. Kendall Jozef L. Teugels Stochastic Geometry and Its Applications Second Edition Dietrich Stoyan, TU Bergakademie Freiberg, Germany Wilfrid S. Kendall, University of Warwick, UK Joseph Mecke, Friedrich-Schiller-Universitat Jena, Germany This standard text makes the results and methods of stochastic geometry and spatial statistics accessible to practitioners and non-theoreticians. The book is also ideal as an introduction to the subject for mathematicians. The exposition is mathematically precise and takes into account the latest results, but in many cases proofs are omitted. Topics covered include the basic theories of point processes, random sets, fibre and surface processes, random tessellations, stereology and the statistical theory of shape. The theory is illustrated by many examples drawn from different branches of science; actual data in the form of images are presented, and their statistical analysis is discussed. As well as being of great interest to statisticians, this treatment of the subject has proved useful to applied scientists working in fields such as geology, biology, microscopy and materials science, and to pure mathematicians working in geometry. 1995 Bayesian Analysis in Statistics and Econometrics. Essays in Honor of Arnold Zellner Donald A. Berry, Duke University Kathryn M. Chaloner, University of Minnesota John K. Geweke, University of Minnesota This volume affords students and professionals in statistics, econometrics, and other fields of statistical applications a unique opportunity to acquaint themselves with important current and future trends in Bayesian analytical theory and practice. Over the course of forty-eight chapters, more than one hundred authors from around the world explore a vast array of practical and theoretical issues. Topics covered include inference, estimation, prediction, regression, linear model, multivariate analysis, model selection and computation. This is a valuable working resource for statisticians, economists, and all those with a professional interest in the Bayesian approach. 1996

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