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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. Each chapter contains exercises that range from simple questions to research problems. Most of the examples are accompanied by demonstration and source code available in from the author's Web site. New in this second edition are demonstrations coded in R, as well as new sections on linear programming and the Nelder-Mead search algorithm.
- Sales Rank: #252569 in Books
- Published on: 2011-04-18
- Released on: 2011-06-09
- Original language: English
- Number of items: 1
- Dimensions: 9.96" h x .98" w x 8.46" l, 1.80 pounds
- Binding: Paperback
- 464 pages
Review
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
About the Author
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).
Most helpful customer reviews
3 of 4 people found the following review helpful.
Really usefull book about numerical math
By Andre M. Strauss
For those who wants to understand what programns like spss, statistica and minitab do. Its not trivial but for the midle level student its very usefull. All the examples are in fortram language what is bad for me... But in general Monahan writes in very clear way, special thing in this kind of book.
2 of 3 people found the following review helpful.
Not Useful
By G. Nelson
While this book may be appropriate for University level students, it is out-of date with respect to the coding languages used and the coding examples only make sense if you get the floppy that is supposed to come with the book and which did not come with the book I ordered. For me, this was a total waste of money. However, for a older grad student who knows Fortran this may be the ticket.
5 of 15 people found the following review helpful.
algebra is not that hard
By A Customer
but at least this book makes it look so. many notations in the book come from nowhere and explained in nowhere, which practically makes it harder to follow what the author's trying to demonstrate. i mean come on, this is a textbook, which means nothing new, you just need to arrange things nicely, and make everything clear.
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