High-Dimensional Statistics: A Non-Asymptotic Viewpoint (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 48)
$76.12
Description
Book Synopsis: 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.
Details
Are you struggling to analyze the massive volumes of data collected in your work or research? Look no further! Introducing High-Dimensional Statistics: A Non-Asymptotic Viewpoint, a groundbreaking book that will revolutionize the way you approach statistical analysis. Written by leading experts in the field, this comprehensive guide is specifically aimed at graduate students and researchers in statistics, machine learning, and related fields.
With its self-contained introduction to high-dimensional statistics, this book provides a solid foundation for tackling the challenges posed by the ever-increasing volume and variety of data in scientific disciplines and industrial settings. You'll learn the core methodology and theory behind high-dimensional statistics, including tail bounds, concentration inequalities, uniform laws, empirical process, and random matrices. Armed with this knowledge, you'll be well-equipped to handle even the most complex data sets with ease.
What sets this book apart is its in-depth exploration of various model classes. From sparse linear models to matrix models with rank constraints, graphical models, and non-parametric models, you'll gain a deep understanding of these specialized models and learn how to apply modern statistical methods to large-scale data with confidence.
Don't miss out on this invaluable resource that combines rigorous theory with practical applications. Whether you're taking a course or studying on your own, High-Dimensional Statistics: A Non-Asymptotic Viewpoint is a must-have for anyone seeking to master statistical analysis in the era of big data.
Ready to take your statistical analysis skills to the next level? Order your copy today!
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