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Essentials of Statistical Inference

Essentials of Statistical Inference

$110.00 (X)

Part of Cambridge Series in Statistical and Probabilistic Mathematics

  • Authors:
  • G. A. Young, Imperial College of Science, Technology and Medicine, London
  • R. L. Smith, University of North Carolina, Chapel Hill
  • Date Published: July 2005
  • availability: Available
  • format: Hardback
  • isbn: 9780521839716

$ 110.00 (X)

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About the Authors
  • This textbook presents the concepts and results underlying the Bayesian, frequentist, and Fisherian approaches to statistical inference, with particular emphasis on the contrasts between them. Aimed at advanced undergraduates and graduate students in mathematics and related disciplines, it covers basic mathematical theory as well as more advanced material, including such contemporary topics as Bayesian computation, higher-order likelihood theory, predictive inference, bootstrap methods, and conditional inference.

    • Very concise account of the fundamental core of statistical inference
    • Gives a broad treatment of its subject, emphasizing both Bayesian and frequentist approaches
    • Emphasizes computational techniques as well as basic theory
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    Reviews & endorsements

    "This is a delightful book! It gives a well-written exposure to inference issues in statistics, very suitable for a first-year graduate course...The authors present the material in a very good pedagogical manner. The examples are excellent, and the exercises are very instructive...very much up to date and includes recent developments in the field."
    MAA Reviews

    "This is a solid book, ideal for advanced classes in the mathematical justification for statistical inference."
    Journal of Recreational Mathematics

    "I wish that I had had such a textbook during my student days...this new book presents the core ideas of statistical inference in the unifying framework of decision theory and includes a fruitful discussion of the different foundational standpoints (Bayesian, Fisherian and frequentist)...[it is] sufficiently precise to satisfy a mathematician and yet omitting too much technical detail that could hide the core of the ideas. Carefully selected examples from a rainbow of application areas such as baseball, coal-mining disasters or gene expression data make it even more enjoyable to read...this book is a very nice graduate level textbook."
    Journal of the Royal Statistical Society

    "[T]his book gives a clear and comprehensive account of the basic elements of statistical theory. It should make a good text for an advanced course on statistical inference...Students will find it informative and challenging."
    ISI Short Book Reviews

    "Essentials of Statistical Inference is a book worth having."
    Jane L. Harvill, Baylor University for the Journal of The American Statistician

    "The book is comprehensively written without dwelling in unnecessary details."
    Iris Pigeot, Biometrics

    "This book is very unique in that the authors present the foundations of all three schools of inference and produce the essential theoretical results in each approach. This text also contains a great bibliography that is partially annotated. Readers should pay attention to the annotations as they are very enlightening. This book could easily be used for a modern first graduate level course in mathematical statistics."
    Michael R. Chernick, Significance

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    Product details

    • Date Published: July 2005
    • format: Hardback
    • isbn: 9780521839716
    • length: 236 pages
    • dimensions: 254 x 178 x 14 mm
    • weight: 0.63kg
    • contains: 92 exercises
    • availability: Available
  • Table of Contents

    1. Introduction
    2. Decision theory
    3. Bayesian methods
    4. Hypothesis testing
    5. Special models
    6. Sufficiency and completeness
    7. Two-sided tests and conditional inference
    8. Likelihood theory
    9. Higher-order theory
    10. Predictive inference
    11. Bootstrap methods.

  • Instructors have used or reviewed this title for the following courses

    • Biostatistical Theory
    • Elements of Probability & Statistics
    • Essentials of Biostatistical Inference
    • Mathematical Statistics
  • Authors

    G. A. Young, Imperial College of Science, Technology and Medicine, London
    G. A. Young is Professor of Statistics at Imperial College London.

    R. L. Smith, University of North Carolina, Chapel Hill
    R. L. Smith is Mark L. Reed Distinguished Professor of Statistics at the University of North Carolina, Chapel Hill.

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