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All of Statistics
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All of Statistics Hardback -

by Larry Wasserman

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  • Hardcover

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Springer , pp. 468 Index. Hardback. New.
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Details

  • Title All of Statistics
  • Author Larry Wasserman
  • Binding Hardback
  • Edition International Ed
  • Condition New
  • Pages 442
  • Volumes 1
  • Language ENG
  • Publisher Springer , New Delhi
  • Date pp. 468 Index
  • Features Bibliography, Index
  • Bookseller's Inventory # 6288028
  • ISBN 9780387402727 / 0387402721
  • Weight 1.73 lbs (0.78 kg)
  • Dimensions 9.28 x 6.6 x 1.05 in (23.57 x 16.76 x 2.67 cm)
  • Library of Congress subjects Mathematical statistics
  • Library of Congress Catalog Number 2003062209
  • Dewey Decimal Code 005.55

From the publisher

This book surveys a broad range of topics in probability and mathematical statistics. It provides the statistical background that a computer scientist needs to work in the area of machine learning.

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From the rear cover

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.

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.

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