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28963291
High-Dimensional Statistics: A Non-Asymptotic Viewpoint (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 48)
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Item #:
28963291
XCD 283
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What Stands Out
Comprehensive Insights
Provides a thorough exploration of high-dimensional statistics, offering non-asymptotic approaches to complex statistical problems, making it invaluable for researchers and students advancing in this critical field.
Practical Applications
Equips readers with practical techniques and methodologies applicable in real-world scenarios, bridging the gap between theory and practice, thus appealing to applied statisticians and data scientists.
Expert Contributions
Authored by leading experts, ensuring high-quality content and cutting-edge perspectives in statistical mathematics, delivering exceptional value for academics and professionals seeking to enhance their knowledge and skills.
Product Details
| Publisher | Cambridge University Press |
| Publication date | February 21, 2019 |
| Edition | 1st |
| Language | English |
| Print length | 568 pages |
| ISBN-10 | 1108498027 |
| ISBN-13 | 978-1108498029 |
| Item Weight | 2.65 pounds (1.2 kg) |
| Dimensions | 7.25 x 1.5 x 10.25 inches (18.4 x 3.8 x 26 cm) |
| Part of series | Cambridge Series in Statistical and Probabilistic Mathematics |
Who Should Buy?
-
Statistical Researchers
Ideal for researchers focused on developing new statistical methods in high-dimensional settings, enhancing their theoretical groundwork.
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Graduate Students
Suitable for graduate students in statistics who require a comprehensive understanding of high-dimensional statistical analysis techniques.
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Data Scientists
Data scientists working with complex datasets will benefit from the insights into high-dimensional statistical challenges and solutions.
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Beginner Statistics
Not ideal for beginners in statistics, as the content assumes prior knowledge of advanced statistical concepts and theories.
Product Description
High-Dimensional Statistics: A Non-Asymptotic Viewpoint (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 48)
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Probability & Statistics Editorial Review
This book is a great reference for econometricians exploring high-dimensional methods. While it is a heavy mathematics text, it is well written and readable. It covers state of the art topics and is recommended for graduate students and researchers in statistics, machine learning, and electrical engineering.
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Pros
- Great reference for econometricians exploring high-dimensional methods
- Well written and readable
- Covers state of the art topics
- Recommended for graduate students and researchers in statistics, machine learning, and electrical engineering
Cons
- Heavy mathematics text
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