Numerical Methods of Statistics


Numerical Methods of Statistics

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.


 Reviews:

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

Review of the hardback: '… 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, Zentralblatt für Mathematik

Review of the hardback: '… 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, Bulletin of the Belgian Mathematical Society

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