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...(展开全部)

This book is an introduction to the field of asymptotic statistics. The treatment is both practical and mathematically rigorous. In addition to most of the standard topics of an asymptotics course, including likelihood inference, M-estimation, the theory of asymptotic efficiency, U-statistics, and rank procedures, the book also presents recent research topics such as semiparame...(展开全部)

Modern statistics deals with large and complex data sets, and consequently with models containing a large number of parameters. This book presents a detailed account of recently developed approaches, including the Lasso and versions of it for various models, boosting methods, undirected graphical modeling, and procedures controlling false positive selections. A special characte...(展开全部)

High-dimensional probability offers insight into the behavior of random vectors, random matrices, random subspaces, and objects used to quantify uncertainty in high dimensions. Drawing on ideas from probability, analysis, and geometry, it lends itself to applications in mathematics, statistics, theoretical computer science, signal processing, optimization, and more. It is the f...(展开全部)

This classic book uses words and diagrams, rather than formulas and equations, to help readers understand what statistics is, and how to think statistically. It focuses on the ideas behind statistics only; readers are not required to perform any calculations.

What you'll find in this monograph is nothing less than a complete and rigorous study of modern functional analysis. It is intended for the student or researcher who could benefit from functional analytic methods, but who does not have an extensive background in the subject and does not plan to make a career as a functional analyst. It develops the topological structures in con...(展开全部)

Modern statistics is very different from the dry and dusty discipline of the popular imagination. In its place is an exciting subject which uses deep theory and powerful software tools to shed light and enable understanding. And it sheds this light on all aspects of our lives, enabling astronomers to explore the origins of the universe, archaeologists to investigate ancient civ...(展开全部)

The book covers new mathematical and computational principles for high-dimensional data analysis (statistics and geometry), scalable optimization methods (convex and nonconvex), and important applications such as scientific imaging, wideband communications, face recognition, 3D vision, and deep networks. This book is to be used as an introductory graduate textbook for the areas...(展开全部)

弗里德曼于 1938 年 3 月 5 日出生于加拿大魁北克省蒙特利尔。他于 1958 年获得麦吉尔大学理学学士学位，并分别于 1959 年和 1960 年获得普林斯顿大学硕士和博士学位。他于 1961 年加入加州大学伯克利分校统计系担任讲师，并于 1962 年被任命为研究员。他的整个职业生涯都在伯克利大学度过。他以概率论者和数理统计学家的身份开始职业生涯，具有贝叶斯倾向，但后来成为世界领先的应用统计学家和谨慎的频率论者之一。