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  3. Statistical Models
  • Cited by 363
Publisher:
Cambridge University Press
Online publication date:
March 2011
Print publication year:
2003
Online ISBN:
9780511815850
DOI:
https://doi.org/10.1017/CBO9780511815850
Series:
Cambridge Series in Statistical and Probabilistic Mathematics (11)
Subjects:
General Statistics and Probability, Statistical Theory and Methods, Statistics and Probability
Information

Book description

Models and likelihood are the backbone of modern statistics. This 2003 book gives an integrated development of these topics that blends theory and practice, intended for advanced undergraduate and graduate students, researchers and practitioners. Its breadth is unrivaled, with sections on survival analysis, missing data, Markov chains, Markov random fields, point processes, graphical models, simulation and Markov chain Monte Carlo, estimating functions, asymptotic approximations, local likelihood and spline regressions as well as on more standard topics such as likelihood and linear and generalized linear models. Each chapter contains a wide range of problems and exercises. Practicals in the S language designed to build computing and data analysis skills, and a library of data sets to accompany the book, are available over the Web.

Reviews

‘This is an excellent textbook on modern statistics.’

Source: Zentralblatt MATH

‘… an almost encyclopaedic survey of modern parametric statistics. In my view this mammoth and scholarly undertaking invites comparison with Kendall's original Advanced Theory of Statistics, providing as it does a snapshot of the discipline at the present time. Anybody who is seriously involved in the theory or practice of statistics would be well advised to ensure that they have access to a copy.’

Source: International Statistical Institute

‘The volume presents a comprehensive treatment of modern parametric statistical inference. The exposition is concise; instead of giving detailed (technical) proofs, the author prefers to sketch the underlying concepts and gives references, if necessary. I highly recommend this book to anyone who is seriously engaged in the statistical analysis of data or in teaching statistics.’

Walter Schill - University of Bremen

‘We know of no other one book like this one. We love owning this book. It gets placed on our shelf among our favourite reference books … We actually learned a lot and deepened our understanding of many topics while reading Davison's explanations … if asked to summarize Statistical Models in a single word, ‘complete‘ would serve as the only plausible answer.’

Source: Technometrics

'I like this book a lot. It is really a pleasure to read. The 700 pages offer a good initial synopsis of what is going on in modern statistics. The book is lively, full of data, and packed with ideas. The author has put a lot of energy, effort, care, and intellectual input into the book. I would definitely recommend this text, both to students and to colleagues.'

Source: The American Statistician

'A student who has absorbed the contents of this book will be well-prepared to face the statistical world and any instructor would be well-advised to consider using it as a text. The style is good too, so that parts of the book are within the reach of nature undergraduates.'

Source: Mathematical Gazette

'The volume presents a comprehensive treatment of modern parametric statistical inference. The exposition is concise; instead of giving detailed (technical) proofs, the author prefers to sketch the underlying concepts and gives references, if necessary. The numerous examples are taken from a variety of fields and are lively discussed. The book is accompanied by practical analyses in S or R that can be downloaded from the author's website and make it even more useful, also for teaching purposes … I highly recommend this book to anyone who is seriously engaged in the statistical analysis of data or in teaching statistics.'

Source: Biometrics

' … comprehensive and well written … an excellent reference book for health researchers who are unfamiliar with details of any statistical methodology.'

Ramalingam Shanmugam - Texas State University

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