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A comprehensive, classroom-tested introduction to modern computational statistics
This comprehensive introduction enables readers to develop a multifaceted and thorough knowledge of modern statistical computing and computational statistics. Backed by many years of classroom experience, the authors help readers gain a practical understanding of how and why modern statistical methods work, enabling readers to apply these methods effectively. Detailed examples are drawn from diverse fields such as bioinformatics, ecology, medicine, computer vision, and stochastic finance.
The text emphasizes areas that are central to understanding the evolving field of computational statistics including areas where routine application of software often fails to solve complex problems. Topics covered include ordinary and combinatorial optimization, algorithms for missing data, numerical and Monte Carlo integration, simulation, introductory and advanced Markov chain Monte Carlo, bootstrapping, density estimation, and smoothing.
Knowledge of computer languages is not required, making examples and algorithms easier for readers to follow. Everything needed to quickly learn and apply the material is provided and is presented in a fluid, jargon-free style with fascinating real-world examples and problem sets that have been tested in the classroom for more than a decade.
Computational Statistics is recommended for graduate-level courses in statistics, computer science, mathematics, engineering, and other quantitative sciences. Advanced undergraduate students can also use this text to learn the basics and for deeper study as they progress. Chapters are written to stand independently, allowing instructors to build their own courses by selecting topics. Statisticians and quantitative empirical scientists will refer to this desktop reference often. By providing readers with a thorough understanding of contemporary statistical techniques, the book gives readers a solid foundation for contributing their own ideas and finding new applications for this dynamic field.
- Sales Rank: #1289995 in Books
- Published on: 2005-02-02
- Original language: English
- Number of items: 1
- Dimensions: 9.23" h x 1.06" w x 6.46" l, 1.63 pounds
- Binding: Hardcover
- 448 pages
Review
"I would have no hesitation recommending it to working statisticians and quantitative empirical scientists." (Journal of Statistical Software, March 2007)
"Researchers in this field will find this book a very valuable desk-top reference. Instructors will find a wealth of well worked out examples...I strongly recommend this book to anybody interested in statistical computing." (Statistical Methods in Medical Research, October 2006)
"Givens and Hoeting are to be commended for attempting a very ambitious task…" (Journal of the American Statistical Association, June 2006)
"It is incredibly well written and comprehensive…Congratulations to the authors for constructing an excellent text." (Technometrics, May 2006)
"This is an excellent first edition of a text that I hope to use the next time I teach a statistical computing course." (Journal of Statistical Software, April 2005)
"This book is well-written and will be helpful for anyone working in the field of computational statistics…" (Statistical Papers, Vol.48, 2007)
From the Back Cover
A comprehensive, classroom-tested introduction to modern computational statistics
This comprehensive introduction enables readers to develop a multifaceted and thorough knowledge of modern statistical computing and computational statistics. Backed by many years of classroom experience, the authors help readers gain a practical understanding of how and why modern statistical methods work, enabling readers to apply these methods effectively. Detailed examples are drawn from diverse fields such as bioinformatics, ecology, medicine, computer vision, and stochastic finance.
The text emphasizes areas that are central to understanding the evolving field of computational statistics including areas where routine application of software often fails to solve complex problems. Topics covered include ordinary and combinatorial optimization, algorithms for missing data, numerical and Monte Carlo integration, simulation, introductory and advanced Markov chain Monte Carlo, bootstrapping, density estimation, and smoothing.
Knowledge of computer languages is not required, making examples and algorithms easier for readers to follow. Everything needed to quickly learn and apply the material is provided and is presented in a fluid, jargon-free style with fascinating real-world examples and problem sets that have been tested in the classroom for more than a decade.
Computational Statistics is recommended for graduate-level courses in statistics, computer science, mathematics, engineering, and other quantitative sciences. Advanced undergraduate students can also use this text to learn the basics and for deeper study as they progress. Chapters are written to stand independently, allowing instructors to build their own courses by selecting topics. Statisticians and quantitative empirical scientists will refer to this desktop reference often. By providing readers with a thorough understanding of contemporary statistical techniques, the book gives readers a solid foundation for contributing their own ideas and finding new applications for this dynamic field.
About the Author
GEOF H. GIVENS, PHD, and JENNIFER A. HOETING, PHD, are both Associate Professors in the Department of Statistics, Colorado State University. Dr. Givens is a past recipient of the Outstanding Statistical Application Award from the American Statistical Association and a CAREER grant awarded by the National Science Foundation. His interests include wildlife population dynamics modeling, Bayesian methods, computerized face recognition, and bioinformatics. Dr. Hoeting is an award-winning teacher who helps lead large research efforts funded by the U.S. Environmental Protection Agency and the National Science Foundation, and she serves as Associate Editor for the Journal of the American Statistical Association. Her research interests include Bayesian methods, model selection, and spatial statistics.
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