All Of Statistics A Concise Course in Statistical Inference
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- Engels
- Hardcover
- 9780387402727
- 04 december 2003
- 442 pagina's
Samenvatting
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.
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.
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Inhoud
- Taal
- en
- Bindwijze
- Hardcover
- Oorspronkelijke releasedatum
- 04 december 2003
- Aantal pagina's
- 442
- Illustraties
- Nee
Betrokkenen
- Hoofdauteur
- Larry Wasserman
- Hoofduitgeverij
- Springer-Verlag New York Inc.
Overige kenmerken
- Editie
- 1st Corrected ed. 2004. Corr. 2nd printing 2004
- Extra groot lettertype
- Nee
- Product breedte
- 159 mm
- Product hoogte
- 25 mm
- Product lengte
- 235 mm
- Studieboek
- Ja
- Verpakking breedte
- 166 mm
- Verpakking hoogte
- 32 mm
- Verpakking lengte
- 241 mm
- Verpakkingsgewicht
- 937 g
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- EAN
- 9780387402727
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