Models and likelihood are the backbone of modern statistics and data analysis. The coverage 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. Anthony Davison blends theory and practice to provide an integrated text for advanced undergraduate and graduate students, researchers and practicioners. Its comprehensive coverage makes this the standard text and reference in the subject.
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Book Description
Editorial Reviews
Review
"Davison provides sound statistical tools to tackle applications in a concise, succinct tutelage, yet without abandoning mathematical sophistication or detail...a wonderfully rich treatise of cleverly attended-to examples and charming asides...we actually learned a lot and deepened our understanding of many topics."
Technometrics
"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."
Biometrics
"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."
The American Statistician
"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."
International Statistical Institute
"...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
"...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
Technometrics
"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."
Biometrics
"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."
The American Statistician
"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."
International Statistical Institute
"...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
"...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
Book Description
Models and likelihood are the backbone of modern statistics and data analysis. Anthony Davison here blends theory and practice to provide an integrated text for advanced undergraduate and graduate students, researchers and practitioners. The coverage 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. This combination will ensure that this becomes the standard text and reference in the subject.
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5 of 5 people found the following review helpful:
4.0 out of 5 stars
Good book on statical models,
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This review is from: Statistical Models (Cambridge Series in Statistical and Probabilistic Mathematics) (Paperback)
The book is well worth reading, especially for those of us who are not well versed in matematics and matematical formalism. Some background in basic frequentist and/or Bayesian statistics is needed. Otherwise the book is easy to read and the real life application of the examples is easy to apply.
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Inside This Book
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First Sentence:
Statistics concerns what can be learned from data. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
annual maximum sea levels, nodal involvement data, toxoplasmosis data, whose rth element, group transformation model, iterative weighted least squares algorithm, log likelihood contribution, maize data, profile log likelihood, poisons data, normal linear model, partial correlogram, jth case, normal random sample, thermal distress, cement data, repeated sampling interpretation, independent exponential variables, modified profile likelihood, approximate pivot, adding potash, expected information matrices, moral graph, full conditional densities, natural exponential family Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Bayesian Models, Nonlinear Regression Models, Stochastic Models, Variety Yield, City Rain, Dynamo Tyre, Introduction Table, Karl Pearson, P-value Pobs, Semiparametric Regression Figure New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Statistics concerns what can be learned from data. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
annual maximum sea levels, nodal involvement data, toxoplasmosis data, whose rth element, group transformation model, iterative weighted least squares algorithm, log likelihood contribution, maize data, profile log likelihood, poisons data, normal linear model, partial correlogram, jth case, normal random sample, thermal distress, cement data, repeated sampling interpretation, independent exponential variables, modified profile likelihood, approximate pivot, adding potash, expected information matrices, moral graph, full conditional densities, natural exponential family Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Bayesian Models, Nonlinear Regression Models, Stochastic Models, Variety Yield, City Rain, Dynamo Tyre, Introduction Table, Karl Pearson, P-value Pobs, Semiparametric Regression Figure New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 100
books:
See all 100 books this book cites
100
books
cite this book:
See all 100 books citing this book
- Graphical Models (Oxford Statistical Science Series) by Steffen L. Lauritzen in Back Matter (1), and Back Matter (2)
- Exploring Data Tables, Trends, and Shapes (Wiley Series in Probability and Statistics) by David C. Hoaglin in Back Matter (1), and Back Matter (2)
- Linear Models (Wiley Classics Library) by Shayle R. Searle in Back Matter (1), and Back Matter (2)
- Bootstrap Methods and their Application (Cambridge Series in Statistical and Probabilistic Mathematics) by A. C. Davison in Front Matter, and Back Matter
- Markov Chains (Cambridge Series in Statistical and Probabilistic Mathematics) by J. R. Norris in Front Matter, and Back Matter
See all 100 books this book cites
- Statistical Computing: An Introduction to Data Analysis using S-Plus by Michael J. Crawley on 4 pages
- Essentials of Statistical Inference (Cambridge Series in Statistical and Probabilistic Mathematics) by G. A. Young in Front Matter, and Back Matter
- Asymptotic Theory of Statistical Inference for Time Series (Springer Series in Statistics) by Masanobu Taniguchi in Front Matter, and Back Matter
- Semiparametric Theory and Missing Data (Springer Series in Statistics) by Anastasios Tsiatis in Front Matter, and Back Matter
- Bayesian Survival Analysis by Joseph G. Ibrahim in Front Matter, and Back Matter
See all 100 books citing this book
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