Cambridge University Press
Numerical Methods of Statistics
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Title: Numerical Methods of Statistics
Author: John F Monahan
ISBN: 9780521791687
Publisher: Cambridge University Press
Published: 2001
Binding: Hardcover
Language: English
Edition: 1
Number of Pages: 444
Condition Note: Clean, unmarked copy with some edge wear. Good binding. Dust jacket included if issued with one. We ship in recyclable American-made mailers. 100% money-back guarantee on all orders.
Publisher 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. 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.
Author: John F Monahan
ISBN: 9780521791687
Publisher: Cambridge University Press
Published: 2001
Binding: Hardcover
Language: English
Edition: 1
Number of Pages: 444
Condition Note: Clean, unmarked copy with some edge wear. Good binding. Dust jacket included if issued with one. We ship in recyclable American-made mailers. 100% money-back guarantee on all orders.
Publisher 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. 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.
