Numerical Methods of Statistics (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 7) - Softcover

Review of the hardback: '... 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 hat 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, Mathematics of Computing

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

This 2001 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 provides a basic background in numerical analysis emphasizing 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 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 from the simple to research problems, as well as examples of the methods at work.