- Type: Hardcover Book.
- Publisher: Cambridge University Press
- Language : English
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High-Dimensional Statistics: A Non-Asymptotic Viewpoint, 1st Edition (ISBN 9781108498029)
High-Dimensional Statistics: A Non-Asymptotic Viewpoint by Martin J. Wainwright is a landmark graduate-level text that reshapes how modern statistical inference is taught and understood. Part of the prestigious Cambridge Series in Statistical and Probabilistic Mathematics, this volume provides a rigorous yet accessible foundation for non-asymptotic methods used widely in machine learning, probability, and high-dimensional data analysis.
Product Details
- Title: High-Dimensional Statistics: A Non-Asymptotic Viewpoint
- Edition: 1st Edition
- Series: Cambridge Series in Statistical and Probabilistic Mathematics
- Author: Martin J. Wainwright
- ISBN-13: 9781108498029
- Format: Paperback
- Price: $49.99
- Availability: In Stock | Free Shipping
About This Book
This book presents a unified and rigorous non-asymptotic framework for statistical inference in high-dimensional settings. It blends probability, optimization, and statistical learning theory, making it essential for researchers and graduate students working in data science, applied mathematics, and theoretical machine learning.
Key Features
- Non-Asymptotic Foundations: Develops tools for analyzing estimators in finite-sample, high-dimensional environments.
- Concentration Inequalities: Comprehensive treatment of Chernoff, Bernstein, Hoeffding, and related inequalities.
- Applications to Modern Inference: Covers Lasso, matrix estimation, convex optimization, and empirical processes.
- Bridges Classical and Modern Statistics: Provides theory relevant to machine learning, signal processing, and probabilistic modeling.
- Ideal for Graduate Courses: Widely used in statistics, computer science, and applied math programs.
Topics Covered
- Concentration of Measure
- Random Matrices and Spectral Theory
- High-Dimensional Parameter Estimation
- Sparse Regression and the Lasso
- Low-Rank Matrix Recovery
- High-Dimensional Hypothesis Testing
- Convexity and Optimization in Statistics
- Empirical Processes and Complexity Measures
Who Should Use This Book?
- Graduate students in statistics, data science, and machine learning
- Researchers in probability, optimization, and applied mathematics
- Machine learning theorists and algorithm developers
- Professionals working with large-scale or high-dimensional datasets
Why Order from BooksGoat?
- Best Price: $49.99
- Free Shipping on all orders
- Research-Grade Inventory: Ideal for graduate seminars and labs
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Summary
High-Dimensional Statistics: A Non-Asymptotic Viewpoint is an essential text for anyone advancing in theoretical statistics or machine learning. Order today with free shipping.
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