Computational Statistics
by Geof H. Givens and Jennifer A. Hoeting
| A comprehensive text on modern and classical methods of statistical computing and computational statistics with detailed examples and problems drawn from diverse fields including bioinformatics, ecology, medicine, computer vision, and stochastic finance. | ||
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Short courses for scientists, statisticians, and professionals:
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Contributions from readers:
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| Code
Here are files with code and examples in the R language. These include complete code to replicate many of our examples and figures. We also provide additional examples and new coding exercises. This is a .zip file with several text files with code and comments, some data and libraries, and an overview document. |
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| Datasets
Datasets are provided as space-delimited ascii files with a header row of column names. A corresponding text file describing each dataset is also provided. A zipped archive of all files can be obtained here. Click on the download buttons ( |
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| Dataset | Chapter | Reference | Data | Description |
| All data | 1-12 | Includes descriptions | ||
| San Fancisco weather | 1 | Example 1.3 | ||
| Human face recognition | 2, 12 | Example 2.5, Problem 12.4 | ||
| Leukemia remissions | 2 | Problem 2.3 | ||
| Oil spills | 2 | Problem 2.5 | ||
| Flour beetles | 2 | Problem 2.6 | ||
| Baseball salaries | 3, 8 | Examples 3.3, 3.5, 3.6, 3.7, 8.4 Problems 3.1, 3.2, 3.3, 3.4 | ||
| Genetic mapping (small) | 3 | Example 3.4, Problems 3.5, 3.6 | ||
| Genetic mapping (large) | 3 | Problem 3.7 | ||
| Wine chemistry | 3 | Problem 3.8 | ||
| Censored data | 4 | Example 4.7 | ||
| HIV risk | 4 | Problem 4.2 | ||
| Trivariate normal | 4 | Problem 4.3 | ||
| Gear coupling failures | 4 | Problem 4.4 | ||
| Coin flips for Baum-Welch | 4 | Problem 4.5 | ||
| Alzheimer's disease | 5 | Examples 5.1, 5.2, 5.3, 5.4, 5.5 | ||
| Coal mining disasters | 6, 7 | Problems 6.4, 7.6 | ||
| Mixture distribution | 7 | Examples 7.2, 7.3, 7.5, Problem 7.2 | ||
| Fur seal pups | 7 | Section 7.4 | ||
| Breast cancer | 7 | Problem 7.5 | ||
| Pigment moisture | 7 | Problem 7.8 | ||
| Utah serviceberry | 8 | Examples 8.6, 8.7, 8.9 | ||
| Linear 'image' | 8 | Problem 8.4 | ||
| X 'image' | 8 | Problem 8.5 | ||
| Copper-nickel alloy | 9 | Examples 9.3, 9.4, 9.5, 9.6, 9.7, 9.8 | ||
| Salmon population | 9 | Problem 9.4 | ||
| Cancer survival | 9 | Problem 9.5 | ||
| Bimodal density | 10 | Examples 10.1, 10.5 | ||
| Whale migration | 10 | Examples 10.2, 10.3, 10.4, 10.6 | ||
| Bivariate rotation | 10 | Example 10.8 | ||
| Infrared emissions | 10 | Problems 10.1, 10.2 | ||
| Manifold data | 10 | Problem 10.5 | ||
| Easy smoothing | 11 | Figure 11.1, Examples 11.1, 11.2, 11.3, 11.4, 11.5, 11.7 | ||
| Difficult smoothing | 11 | Example 11.8 | ||
| General bivariate curve | 11, 12 | Figure 11.18, Example 12.7, Problem 12.8 | ||
| Martian atmosphere | 11 | Problem 11.4 | ||
| Martian atmosphere - all | 11 | Problem 11.4 | ||
| Air blast pressure | 11 | Problems 11.6, 11.7 | ||
| Norwegian paper plant | 12 | Examples 12.1, 12.3 | ||
| Drug abuse | 12 | Example 12.2 | ||
| Stream monitoring | 12 | Examples 12.4, 12.5, 12.6, Problem 12.5 | ||
| Body fat | 12 | Problems 12.2, 12.3 |
| Reviews: | |||||||
| "The authors write beautifully" | |||||||
| --David W. Scott, Rice University, past editor of Journal of Computational and Graphical Statistics and Journal of Computational Statistics | |||||||
| "I have adopted your book as a text for my class. I have taught different versions of this course since 1989 and your book covers just the right material for me with lots of real examples. I am enjoying it a lot. Congratulations on publishing it at less than $100!" | |||||||
| --Susan Holmes, Stanford University | |||||||
| "This book will be a terrific reference for workers in the field" | |||||||
| --Michael Newton, Univ. of Wisconsin, 2004 COPSS Presidents' Award recipient | |||||||
| "This book includes more of the topics that I think are crucial for statistical computing than any other text I've encountered" | |||||||
| --Kate Cowles, Univ. of Iowa | |||||||
| "The book includes a solid theoretical background at the introductory graduate level, practical advice, application to real datasets, and very few errors. It covers a large selection of topics very well. ...This is an excellent first edition of a text that I hope to use the next time I teach a statistical computing course." | |||||||
| --Duncan Murdoch, Univ. of Western Ontario, reviewing the book for Journal of Statistical Software | |||||||