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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 selfcontained introduction to the area of highdimensional statistics, aimed at the firstyear 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 indepth exploration of particular model classes including sparse linear models, matrix models with rank constraints, graphical models, and various types of nonparametric models. With hundreds of worked examples and exercises, this text is intended both for courses and for selfstudy by graduate students and researchers in statistics, machine learning, and related fields who must understand, apply, and adapt modern statistical methods suited to largescale data.
⚠️ WARNING (California Proposition 65):
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birth defects, or other reproductive harm.
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