Wasserman, Larry A.
All of Statistics: A Concise Course in Statistical Inference
### ISBN 13: 9780387402727

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.

*Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.*

Winner of the 2005 DeGroot Prize.

From the reviews:

"Presuming no previous background in statistics and described by the author as "demanding" yet "understandable because the material is as intuitive as possible" (p. viii), this certainly would be my choice of textbook if I was required to learn mathematical statistics again for a couple of semesters." *Technometrics, August 2004*

"This book should be seriously considered as a text for a theoretical statsitics course for non-majors, and perhaps even for majors...The coverage of emerging and important topics is timely and welcomed...you should have this book on your desk as a reference to nothing less than 'All of Statistics.'" *Biometrics, December 2004*

"Although *All of Statistics* is an ambitious title, this book is a concise guide, as the subtitle suggests....I recommend it to anyone who has an interest in learning something new about statistical inference. There is something here for everyone." *The American Statistician, May 2005*

"As the title of the book suggests, ‘All of Statistics’ covers a wide range of statistical topics. … The number of topics covered in this book is vast … . The greatest strength of this book is as a first point of reference for a wide range of statistical methods. … I would recommend this book as a useful and interesting introduction to a large number of statistical topics for non-statisticians and also as a useful reference book for practicing statisticians." (Matthew J. Langdon, Journal of Applied Statistics, Vol. 32 (1), January, 2005)

"This book was written specifically to give students a quick but sound understanding of modern statistics, and its coverage is very wide. … The book is extremely well done … ." (N. R. Draper, Short Book Reviews, Vol. 24 (2), 2004)

"This is most definitely a book about mathematical statistics. It is full of theorems and proofs … . Presuming no previous background in statistics … this certainly would be my choice of textbook if I was required to learn mathematical statistics again for a couple of semesters." (Eric R. Ziegel, Technometrics, Vol. 46 (3), August, 2004)

"The author points out that this book is for those who wish to learn probability and statistics quickly … . this book will serve as a guideline for instructors as to what should constitute a basic education in modern statistics. It introduces many modern topics … . Adequate references are provided at the end of each chapter which the instructor will be able to use profitably … ." (Arup Bose, Sankhya, Vol. 66 (3), 2004)

"The amount of material that is covered in this book is impressive. … the explanations are generally clear and the wide range of techniques that are discussed makes it possible to include a diverse set of examples … . The worked examples are complemented with numerous theoretical and practical exercises … . is a very useful overview of many areas of modern statistics and as such will be very useful to readers who require such a survey. Library copies would also see plenty of use." (Stuart Barber, Journal of the Royal Statistical Society, Series A – Statistics in Society, Vol. 168 (1), 2005)

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.

*Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.*

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**Descrizione libro **Condizione libro: New. This item is printed on demand. Codice libro della libreria 2073373-n

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**Descrizione libro **Springer-Verlag New York Inc. 2004-09-15, New York, NY, 2004. 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. 236 x 157 mm. 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 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. 1st Corrected ed. 2004. Corr. 2nd printing 2004. Codice libro della libreria AAZ9780387402727

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Springer-Verlag New York Inc., United States
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ISBN 10: 0387402721
ISBN 13: 9780387402727

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**Descrizione libro **Springer-Verlag New York Inc., United States, 2005. Hardback. Condizione libro: New. 236 x 157 mm. 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 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. 1st Corrected ed. 2004. Corr. 2nd printing 2004. Codice libro della libreria AAZ9780387402727

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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, 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. . . . . . . Codice libro della libreria V9780387402727

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**Descrizione libro **Springer, 2004. Hardcover. Condizione libro: New. Brand New Book. Shipping: Once your order has been confirmed and payment received, your order will then be processed. The book will be located by our staff, packaged and despatched to you as quickly as possible. From time to time, items get mislaid en route. If your item fails to arrive, please contact us first. We will endeavour to trace the item for you and where necessary, replace or refund the item. Please do not leave negative feedback without contacting us first. All orders will be dispatched within two working days. If you have any quesions please contact us. Codice libro della libreria V9780387402727

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**Descrizione libro **Springer, 2004. Condizione libro: New. Brand New, Unread Copy in Perfect Condition. A+ Customer Service! Summary: This textbook can be used for a course for advanced undergraduates or M.A.-level courses in statistics and computer science departments. It will also serve as a reference for practitioners in machine learning. Codice libro della libreria ABE_book_new_0387402721

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