High-Dimensional Statistics: A Non-Asymptotic Viewpoint (Hardcover)

Martin J. Wainwright

  • 出版商: Cambridge
  • 出版日期: 2019-02-21
  • 售價: $1,680
  • 貴賓價: 9.8$1,646
  • 語言: 英文
  • 頁數: 555
  • 裝訂: Hardcover
  • ISBN: 1108498027
  • ISBN-13: 9781108498029
  • 相關分類: 機率統計學 Probability-and-statistics
  • 下單後立即進貨 (約5~7天)

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商品描述

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.