High-Dimensional Statistics A Non-Asymptotic Viewpoint
High-Dimensional Statistics: A Non-Asymptotic Viewpoint by Martin J. Wainwright, published by Cambridge University Press on February 21, 2019, is a comprehensive resource for understanding high-dimensional statistics. This 552-page book serves as a self-contained introduction aimed at first-year graduate students, addressing the challenges posed by massive data sets in various scientific and industrial contexts. It covers essential methodologies and theories, including tail bounds, concentration inequalities, and random matrices, while also exploring specific model classes such as sparse linear models and graphical models.
Readers will find a structured approach that combines theoretical insights with practical applications, featuring hundreds of worked examples and exercises. This text is designed for both coursework and self-study, making it suitable for graduate students and researchers in statistics, machine learning, and related fields. The content emphasizes modern statistical methods tailored for large-scale data, providing a solid foundation for those looking to navigate the complexities of data science and analytics.
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Recent years have witnessed an explosion in the volume and variety of data collected in all scientific disciplines and industrial settings. Such massive data sets present a number of challenges to researchers in statistics and machine learning. This book provides a self-contained introduction to the area of high-dimensional statistics, aimed at the first-year graduate level. It includes chapters that are focused on core methodology and theory – including tail bounds, concentration inequalities, uniform laws and empirical process, and random matrices – as well as chapters devoted to in-depth exploration of particular model classes – including sparse linear models, matrix models with rank constraints, graphical models, and various types of non-parametric models. With hundreds of worked examples and exercises, this text is intended both for courses and for self-study by graduate students and researchers in statistics, machine learning, and related fields who must understand, apply, and adapt modern statistical methods suited to large-scale data.
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