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Numerical Methods of Statistics

Numerical Methods of Statistics

$32.00 ( ) USD

Part of Cambridge Series in Statistical and Probabilistic Mathematics

  • Date Published: May 2012
  • availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
  • format: Adobe eBook Reader
  • isbn: 9781139244398

$ 32.00 USD ( )
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About the Authors
  • 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. The book concludes with an examination of sorting, FFT and the application of other "fast" algorithms to statistics. Each chapter contains exercises that range in difficulty as well as examples of the methods at work. Most of the examples are accompanied by demonstration code available from the author's home page.

    • Lots of exercises, ranging from elementary to research-level problems
    • Accompanying computer code
    • Useful both as text or reference book
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    Reviews & endorsements

    " extremely readable book. This would be an excellent book for a graduate-level course in statistical computing." Journal of the American Statistical Association

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    Product details

    • Date Published: May 2012
    • format: Adobe eBook Reader
    • isbn: 9781139244398
    • availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
  • Table of Contents

    1. Algorithms and computers
    2. Computer arithmetic
    3. Matrices and linear equations
    4. More methods for solving linear equations
    5. Least squares
    6. Eigenproblems
    7. Functions: interpolation, smoothing and approximation
    8. Introduction to optimization and nonlinear equations
    9. Maximum likelihood and nonlinear regression
    10. Numerical integration and Monte Carlo methods
    11. Generating random variables from other distributions
    12. Statistical methods for integration and Monte Carlo
    13. Markov chain Monte Carlo methods
    14. Sorting and fast algorithms.

  • Author

    John F. Monahan, North Carolina State University

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