All of Statistics: a concise course in statistical inference
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer New York
2004
|
| Schriftenreihe: | Springer Texts in Statistics
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | 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 |
| Beschreibung: | 1 Online-Ressource (XX, 442 p) |
| ISBN: | 9780387217369 |
| ISSN: | 1431-875X |
| DOI: | 10.1007/978-0-387-21736-9 |
Internformat
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Datensatz im Suchindex
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| discipline | Mathematik Wirtschaftswissenschaften |
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| id | DE-604.BV042418971 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-20T17:00:36Z |
| institution | BVB |
| isbn | 9780387217369 |
| issn | 1431-875X |
| language | English |
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| spelling | Wasserman, Larry 1959- Verfasser (DE-588)128738952 aut All of Statistics a concise course in statistical inference by Larry Wasserman New York, NY Springer New York 2004 1 Online-Ressource (XX, 442 p) txt rdacontent c rdamedia cr rdacarrier Springer Texts in Statistics 1431-875X 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 Statistics Computer science Mathematical statistics Statistical Theory and Methods Probability and Statistics in Computer Science Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Informatik Statistik Statistik (DE-588)4056995-0 gnd rswk-swf Statistik (DE-588)4056995-0 s 1\p DE-604 Erscheint auch als Druck-Ausgabe 978-1-4419-2322-6 https://doi.org/10.1007/978-0-387-21736-9 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Wasserman, Larry 1959- All of Statistics a concise course in statistical inference Statistics Computer science Mathematical statistics Statistical Theory and Methods Probability and Statistics in Computer Science Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Informatik Statistik Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4056995-0 |
| title | All of Statistics a concise course in statistical inference |
| title_auth | All of Statistics a concise course in statistical inference |
| title_exact_search | All of Statistics a concise course in statistical inference |
| title_full | All of Statistics a concise course in statistical inference by Larry Wasserman |
| title_fullStr | All of Statistics a concise course in statistical inference by Larry Wasserman |
| title_full_unstemmed | All of Statistics a concise course in statistical inference by Larry Wasserman |
| title_short | All of Statistics |
| title_sort | all of statistics a concise course in statistical inference |
| title_sub | a concise course in statistical inference |
| topic | Statistics Computer science Mathematical statistics Statistical Theory and Methods Probability and Statistics in Computer Science Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Informatik Statistik Statistik (DE-588)4056995-0 gnd |
| topic_facet | Statistics Computer science Mathematical statistics Statistical Theory and Methods Probability and Statistics in Computer Science Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Informatik Statistik |
| url | https://doi.org/10.1007/978-0-387-21736-9 |
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