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  3. Numerical Methods of Statistics
  • Cited by 54
      • 2nd edition
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    • Publisher:
      Cambridge University Press
      Publication date:
      June 2011
      April 2011
      ISBN:
      9780511977176
      9780521191586
      9780521139519
      DOI:
      https://doi.org/10.1017/CBO9780511977176
      Dimensions:
      (253 x 215 mm)
      Weight & Pages:
      1.02kg, 464 Pages
      Dimensions:
      (253 x 215 mm)
      Weight & Pages:
      0.82kg, 464 Pages
      • Series:
      Cambridge Series in Statistical and Probabilistic Mathematics
      Subjects:
      Computational Statistics, Machine Learning and Information Science, General Statistics and Probability, Statistics and Probability
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    Series:
    Cambridge Series in Statistical and Probabilistic Mathematics
    Subjects:
    Computational Statistics, Machine Learning and Information Science, General Statistics and Probability, Statistics and Probability
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    Book description

    This book explains how computer software is designed to perform the tasks required for sophisticated statistical analysis. For statisticians, it examines the nitty-gritty computational problems behind statistical methods. For mathematicians and computer scientists, it looks at the application of mathematical tools to statistical problems. The first half of the book offers a basic background in numerical analysis that emphasizes issues important to statisticians. The next several chapters cover a broad array of statistical tools, such as maximum likelihood and nonlinear regression. The author also treats the application of numerical tools; numerical integration and random number generation are explained in a unified manner reflecting complementary views of Monte Carlo methods. Each chapter contains exercises that range from simple questions to research problems. Most of the examples are accompanied by demonstration and source code available from the author's website. New in this second edition are demonstrations coded in R, as well as new sections on linear programming and the Nelder–Mead search algorithm.

    Reviews

    Review from the previous edition'… an excellent tool both for self-study and for classroom teaching. It summarizes the state of the art well and provides a solid basis, through the programs that go with the book, for numerical experimentation and further development. All in all, this is a good book to have … I recommend it.'

    D. Denteneer Source: Mathematics of Computing

    Review from the previous edition:'… this book grew out of notes for a statistical computing course … The goal of this course was to prepare the doctoral students with the computing tools needed for statistical research. I very much liked this book and recommend it for this use.'

    Jaromir Antoch Source: Zentralblatt für Mathematik

    Review from the previous edition:'… a really nice introduction to numerical analysis. All the classical subjects of a numerical analysis course are discussed in a surprisingly short and clear way … When adapting the examples, the first half of the book can be used as a numerical analysis course for any other discipline …'

    Adhemar Bultheel Source: Bulletin of the Belgian Mathematical Society

    Review from the previous edition:‘… an extremely readable book. This would be an excellent book for a graduate-level course in statistical computing.’

    Source: Journal of the American Statistical Association

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    Contents

    Contents
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