Springer Statistics Statistics for High-Dimensional Data: Methods, Theory and Applications, Paperback
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This valuable compendium of statistical methods features a unique combination of methodology, theory, algorithms and applications.
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What Stands Out
Product Details
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Textbooks |
| Pub date | 20130803 |
| Pages | 558 |
| Reading level | Professional and Scholarly |
| Subgenre | Probability & Statistics |
| Series title | Springer Statistics |
| Number in series | 1 |
| Edition | 2011 Edition |
| Publisher | Springer |
| Original languages | English |
| Language | English |
| Edu focus | Mathematics |
| Is collectible | N |
| Binding type | Perfect Binding |
| Retail packaging | Single Piece |
| Assembled product dimensions (l x w x h) | 6.17 x 1.17 x 9.17 Inches (15.7 x 3 x 23.3 cm) |
| Assembled product weight | 1.76 lb (800 grams) |
| Bisac subject heading | Mathematics |
Who Should Buy?
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Data Scientists
Ideal for data scientists working with high-dimensional datasets seeking advanced statistical methods and applications.
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Researchers
Beneficial for researchers in fields requiring robust statistical analysis, such as genomics or finance, to interpret complex data.
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Graduate Students
Useful for graduate students in statistics or related disciplines who need a comprehensive understanding of high-dimensional statistics.
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Casual Learners
Not suitable for casual learners or hobbyists as it contains advanced concepts that require prior statistical knowledge.
Product Description
Springer Statistics Statistics for High-Dimensional Data: Methods, Theory and Applications, Paperback
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STD 2894653
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Features & Benefits
- Comprehensive overview of statistical methods for high-dimensional data.
- Combines methodology, theory, algorithms, and practical applications.
- Covers advanced techniques like Lasso and boosting methods.
- Essential for handling large and complex data sets.
- Ideal for researchers and practitioners in statistics and data science.
- Published in 2013, ensuring relevance in current analytical practices.
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