All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics)

书籍介绍(英文)

This book is for people who want to learn probability and statistics quickly. It brings together many of the main ideas in modern statistics in one place. The book is suitable for students and researchers in statistics, computer science, data mining and machine learning.

This book covers a much wider range of topics than a typical introductory text on mathematical statistics. It includes modern topics like nonparametric curve estimation, bootstrapping and classification, topics that are usually relegated to follow-up courses. The reader is assumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. The text can be used at the advanced undergraduate and graduate level.

Larry Wasserman is Professor of Statistics at Carnegie Mellon University. He is also a member of the Center for Automated Learning and Discovery in the School of Computer Science. His research areas include nonparametric inference, asymptotic theory, causality, and applications to astrophysics, bioinformatics, and genetics. He is the 1999 winner of the Committee of Presidents of Statistical Societies Presidents’ Award and the 2002 winner of the Centre de recherches mathematiques de Montreal-Statistical Society of Canada Prize in Statistics. He is Associate Editor of The Journal of the American Statistical Association and The Annals of Statistics. He is a fellow of the American Statistical Association and of the Institute of Mathematical Statistics.


这本书的人谁想要学习概率统计迅速。它汇集了在一个地方现代统计的主要想法很多。这本书是适合学生及研究人员统计，计算机科学，数据挖掘和机器学习。

本书涵盖了比一般的数理统计的介绍性文字的主题范围更广。它包括现代主题喜欢非参数曲线估计，引导和分类，即通常沦为后续课程的主题。假定读者了解微积分和线性代数一点。以前没有的知识和概率统计是必需的。文本就可以被用在高年级本科生和研究生的水平。

拉里瓦瑟曼是统计学教授在卡内基梅隆大学。他也是在学校的计算机科学中心的自动学习和发现的成员。他的研究领域包括非参数推断，渐近理论，因果关系，以及天体物理学，应用生物信息学和遗传学。他是对统计学会会长奖总统委员会1999年的冠军，该中心的勘探数学蒙特利尔2002年冠军，加拿大统计学会奖统计。他是副主编对美国统计协会期刊和统计年鉴。他是美国统计协会研究员，数理统计研究所。