Kendall's Advanced Theory of Statistics

Explains important developments such as the use of Markov Chain Monte Carlo methods, bootstrapping and multivariate models. This book discusses key issues such as missing data, measurement errors and multivariate models. It provides real-life examples to illustrate the theoretical concepts.

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Kendall's objective in setting out to write the original Kendall's Advanced Theory of Statistics, published in two volumes in 1943 and 1946, was 'to develop a systematic treatment of [statistical theory] as it exists at the present time.' With this aim in mind, the first edition of Bayesian Inference was added as Volume 2B of the Kendall's Advanced Theory of Statistics in 1994, to introduce the new and rapidly growing field of Bayesian statistics. This new edition is a response to the developments and advances that have taken place in this area over the last few years and offers the reader an up-to-date and comprehensive overview of Bayesian statistics. The second edition of Bayesian Inference has been expanded to include new chapters on Markov Chain Monte Carlo methods, discrete data models and non-parametric models. Existing chapters have also been thoroughly revised and updated and there is greater coverage of computational methods and of model comparison and criticism. There is also a new chapter of case studies, providing practical illustrations of the theory presented throughout the book.Like the other volumes in the Kendall's Library of Statistics, the first edition of Bayesian Inference provided a good selection of exercises at the end of each chapter.
This popular feature is retained in the new edition, with many new exercises to deepen the reader's understanding. Clearly written and offering a wide-ranging introduction to Bayesian statistics, this book will be an essential reference source for students, researchers and practitioners in statistics.