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

Martin J. Wainwright

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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.

商品描述(中文翻譯)

近年來,各個科學領域和工業環境中所收集的數據量和種類呈爆炸性增長。這樣的大規模數據集對統計學和機器學習研究人員提出了一系列挑戰。本書提供了一個自成體系的高維統計學介紹,針對研究生的第一年級水平。它包括一些專注於核心方法和理論的章節 - 包括尾部界限、集中不等式、均勻法則和經驗過程、隨機矩陣 - 以及一些專門探索特定模型類別的章節 - 包括稀疏線性模型、帶有秩限制的矩陣模型、圖模型和各種非參數模型。本書配有數百個實例和練習題,旨在供統計學、機器學習和相關領域的研究生和研究人員使用,他們需要理解、應用和適應適用於大規模數據的現代統計方法。