Numerical methods of statistics (Q5890201)

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. NEWLINENEWLINENEWLINEThe first half of the book provides a basic background in numerical analysis, emphasizing issues that are 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 an unified manner that reflects complementary views of Monte Carlo methods. The book concludes with an examination of sorting, the fast Fourier transform, and the application of other ``fast'' algorithms to statistics. NEWLINENEWLINENEWLINEEach chapter contains exercises that range from the simple to research problems, as well as examples of the methods at work. Most of the examples are accompanied by demonstration code available on a floppy disk included with the book. NEWLINENEWLINENEWLINEAs the author says in the preface, this book grew out of notes for his \textit{Statistical Computing Course} he has been teaching for the past 20 years. The goal of this course was to prepare the doctoral students with the computing tools needed for statistical research. I liked very much this book and must recommend it for this type of the use. NEWLINENEWLINENEWLINEContents: 1. Algorithms and computers; 2. Computer arithmetic; 3. Matrices and linear equations; 4. More methods for solving linear equations; 5. Regression computation; 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; 11. Generating random variables from other distributions; 12. Statistical methods for integration and Monte Carlo; 13. Monte Carlo Markov chains 14. Sorting and fast algorithms.