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

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9780387402727: All of Statistics: A Concise Course in Statistical Inference (Springer Texts in 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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From the Back 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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Larry Wasserman
Editore: Springer New York 2003-12-04, New York (2003)
ISBN 10: 0387402721 ISBN 13: 9780387402727
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Descrizione libro Springer New York 2003-12-04, New York, 2003. hardback. Condizione libro: New. Codice libro della libreria 9780387402727

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Descrizione libro Springer-Verlag New York Inc., United States, 2005. Hardback. Condizione libro: New. Language: English . Brand New Book. 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. 1st Corrected ed. 2004. Corr. 2nd printing 2004. Codice libro della libreria AAZ9780387402727

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Descrizione libro Springer-Verlag New York Inc., United States, 2005. Hardback. Condizione libro: New. Language: English . Brand New Book. 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. 1st Corrected ed. 2004. Corr. 2nd printing 2004. Codice libro della libreria AAZ9780387402727

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Descrizione libro Springer-Verlag New York Inc., 2004. HRD. Condizione libro: New. New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. Codice libro della libreria GB-9780387402727

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Descrizione libro Condizione libro: New. Bookseller Inventory # ST0387402721. Codice libro della libreria ST0387402721

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Descrizione libro Springer-Verlag New York Inc. Hardback. Condizione libro: new. BRAND NEW, All of Statistics: A Concise Course in Statistical Inference (1st Corrected ed. 2004. Corr. 2nd printing 2004), L. A. Wasserman, 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 nonparametric curve estimation, bootstrapping, and clas- sification, 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 analyzing data. For some time, statistics research was con- ducted in statistics departments while data mining and machine learning re- search was conducted in computer science departments. Statisticians thought that computer scientists were reinventing the wheel. Computer scientists thought that statistical theory didn't apply to their problems. Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algo- rithms are more scalable than statisticians ever thought possible. Formal sta- tistical theory is more pervasive than computer scientists had realized. Codice libro della libreria B9780387402727

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Descrizione libro Springer-Verlag New York Inc., 2004. Condizione libro: New. 2004. 1st Corrected ed. 20. Hardcover. Suitable for those who want to learn probability and statistics quickly, this book brings together many of the main ideas in modern statistics. It includes modern topics like nonparametric curve estimation, bootstrapping and classification, topics that are usually relegated to follow-up courses. Series: Springer Texts in Statistics. Num Pages: 442 pages, biography. BIC Classification: PBT. Category: (G) General (US: Trade); (U) Tertiary Education (US: College). Dimension: 162 x 243 x 29. Weight in Grams: 834. . . . . . . Codice libro della libreria V9780387402727

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Descrizione libro Springer, 2004. Condizione libro: New. book. Codice libro della libreria ria9780387402727_rkm

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Descrizione libro Springer-Verlag New York Inc. Condizione libro: New. 2004. 1st Corrected ed. 20. Hardcover. Suitable for those who want to learn probability and statistics quickly, this book brings together many of the main ideas in modern statistics. It includes modern topics like nonparametric curve estimation, bootstrapping and classification, topics that are usually relegated to follow-up courses. Series: Springer Texts in Statistics. Num Pages: 442 pages, biography. BIC Classification: PBT. Category: (G) General (US: Trade); (U) Tertiary Education (US: College). Dimension: 162 x 243 x 29. Weight in Grams: 834. . . . . . Books ship from the US and Ireland. Codice libro della libreria V9780387402727

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Descrizione libro Condizione libro: New. Depending on your location, this item may ship from the US or UK. Codice libro della libreria 97803874027270000000

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