Computational Statistics Hardcover – Feb. 2 2005
Customers who bought this item also bought
No Kindle device required. Download one of the Free Kindle apps to start reading Kindle books on your smartphone, tablet, and computer.
To get the free app, enter your mobile phone number.
- Hardcover : 448 pages
- ISBN-10 : 0471461245
- ISBN-13 : 978-0471461241
- Item Weight : 736 g
- Dimensions : 16.4 x 2.7 x 23.45 cm
- Publisher : Wiley-Interscience; 1st edition (Feb. 2 2005)
- Language: : English
- Best Sellers Rank: #4,564,455 in Books (See Top 100 in Books)
- Customer Reviews:
"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
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.
Top reviews from other countries
but the seller was reliable in sending the book it took about the entire time to get here so if you need it quick you may want to go with someone else. but if you can wait a few days great place to purchase the book.