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。

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。

Praise from students for Neil Salkind's Statistics for People Who (Think They) Hate Statistics: "Your book has changed my perception and attitude towards statistics. It is very well written, the language is completely user friendly and the progression of new concepts within each chapter is fantastic. Although my class is only on chapter 5 at this point, I have found my interest...(展开全部)

Retaining the general organization and style of its predecessor, this new edition continues to serve as a comprehensive guide to modern and classical methods of statistical computing and computational statistics. Approaching the topic in three major parts--optimization, integration, and smoothing--the book includes an overview section in each chapter introduction and step-by-st...(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)

A comprehensive, classroom-tested introduction to modern computational statistics This comprehensive introduction enables readers to develop a multifaceted and thorough knowledge of modern statistical computing and computational statistics. Backed by many years of classroom experience, the authors help readers gain a practical understanding of how and why modern statistical met...(展开全部)

This graduate textbook covers topics in statistical theory essential for graduate students preparing for work on a Ph.D. degree in statistics. This new edition has been revised and updated and in this fourth printing, errors have been ironed out. The first chapter provides a quick overview of concepts and results in measure-theoretic probability theory that are useful in statis...(展开全部)

Robert Keener is Professor of Statistics at the University of Michigan and a fellow of the Institute of Mathematical Statistics.