All of Statistics : A Concise Course in Statistical Inference

Larry Wasserman

ISBN 10: 1441923225 / ISBN 13: 9781441923226

Published by Springer, Humana Dez 2010, 2010

About this Item

Druck auf Anfrage Neuware - Printed after ordering - Taken literally, the title 'All of Statistics' is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines.The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.

Seller Inventory # 9781441923226

Bibliographic Details

• Title: All of Statistics : A Concise Course in Statistical Inference
• Publisher: Springer, Humana Dez 2010
• Publication Date: 2010
• Language: English
• ISBN 10: 1441923225
• ISBN 13: 9781441923226
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 234W x 156H x 26D millimeters 234 millimeters width by 156 millimeters height by 26 millimeters depth
• Weight: 705 grams

About this title

Synopsis

Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. 

The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data. 

About the Author

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.