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High-Dimensional Statistics: A Non-Asymptotic Viewpoint (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 48)
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This book provides a self-contained introduction to the area of high-dimensional statistics, aimed at the first-year graduate level.
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Product Details
- Self-contained introduction to high-dimensional statistics at the first-year graduate level
- Covers core methodology and theory, including tail bounds, concentration inequalities, and empirical process
- Explores specific model classes such as sparse linear models, graphical models, and non-parametric models
- Contains hundreds of worked examples and exercises
- Designed for graduate students and researchers in statistics and machine learning
- Suitable for courses or self-study for understanding modern statistical methods for large-scale data
| 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?
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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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Features & Benefits
- Self-contained introduction to high-dimensional statistics
- Suitable for first-year graduate students and researchers
- Covers core methodology, theory, and model classes with worked examples and exercises.
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