This volume deals with the fundamental theory and principles of Bayesian inference, including estimation, hypothesis testing, and the linear model. The coverage provided overlaps with that of "Kendall's Advanced Theory of Statistics, Volumes 1 and 2" from a Bayesian viewpoint, and cross-referencing is provided where appropriate. Thinking common to both Classical and Bayesian theory is not repeated. A major source and reference, covering advanced level theory at a level which every professional statistician needs to know before specializing, the book also provides an introduction to other major fields which are covered in other books in this series. This book should be of interest to professional statisticians and statistical libraries.
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'A clearly written and comprehensive account...an excellent book in an excellent series.' (Mathematics Today)
This very well-written book has been designed to complement the Kendall's series by presenting therein the Bayesian point of view. ... The author has skilfully managed to cover a great deal of ground in this volume and readers will find few topics of interest to be missing. (Short Book Reviews)
A really important addition to the statistical literature, providing a careful, gentle approach to modern ideas that should find favour as a university text and as a reference book for all of us.
- VOLUME 168 (1) jANUARY 2005 (Journal of the Roayal Statistical Society, Series)
It is now generally recognised in many areas of the social, medical and other sciences that statistical data typically have complex hierarchical or multilevel structures in which individuals are grouped together in communities or institutions. This grouping affects their behaviour and multilevel modelling is now the accepted statistical technique for the analysis of this type of data. An understanding of these methods is vital for researchers in fields such as education, epidemiology, geography, child growth and social surveys, among others. This new edition brings the book fully up to date, explaining important new developments such as the use of Markov Chain Monte Carlo methods, bootstrapping and mulitvariate models. The book has been completely restructured for this third edition and extra space has been given to discussion of key issues such as missing data, measurement errors and multivariate models. Real–life examples are used throughout to illustrate clearly the theoretical concepts.
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Book Description Hodder Education Publishers, 1994. Hardcover. Book Condition: New. Bookseller Inventory # P110340529229