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.
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.
This second edition explains how computer software is designed to perform the tasks required for sophisticated statistical analysis.
John F. Monahan is a Professor of Statistics at North Carolina State University where he joined the faculty in 1978 and has been a professor since 1990. His research has appeared in numerous computational as well as statistical journals. He is also the author of A Primer on Linear Models (2008).
Review from the previous edition '… 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 that
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 from the previous edition: '… 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 from the previous edition: '… 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
Review from the previous edition: '… an extremely readable book.
This would be an excellent book for a graduate-level course in
statistical computing.' Journal of the American Statistical
Association
|
Ask a Question About this Product More... |
|
