Asymptotic Theory of Statistics and Probability (Springer Texts in Statistics)
Language: English
Published by Springer, 2008
- Hardcover
- Used
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Condition: Used - Very good
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Clean Inside With No Remarks Or Highlights. 722 Pages With The Index. Hard Cover Binding. In Very Good Condition. Used Book.We will state signed at the description section. we confirm they are signed via email or stated in the description box. - Specializing in academic, collectiblle and historically significant, providing the utmost quality and customer service satisfaction. For any questions feel free to email us.
Seller Inventory # A9416a
- Title
- Asymptotic Theory of Statistics and Probability (Springer Texts in Statistics)
- Author
- DasGupta, Anirban
- Publisher
- Springer
- Publication year
- 2008
- Condition
- Very Good
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 0387759700
- ISBN 13
- 9780387759708
- Item weight
- 3 ounces
- Dimensions
- 6x1x9
- Series
- Book 31 of 111: Springer Texts in Statistics
- Seller catalogs
- Philosophy, Business & Economics
"Synopsis" may belong to another edition of this title.
From the Back Cover
This book is an encyclopedic treatment of classic as well as contemporary large sample theory, dealing with both statistical problems and probabilistic issues and tools. It is written in an extremely lucid style, with an emphasis on the conceptual discussion of the importance of a problem and the impact and relevance of the theorems. The book has 34 chapters over a wide range of topics, nearly 600 exercises for practice and instruction, and another 300 worked out examples. It also includes a large compendium of 300 useful inequalities on probability, linear algebra, and analysis that are collected together from numerous sources, as an invaluable reference for researchers in statistics, probability, and mathematics.
It can be used as a graduate text, as a versatile research reference, as a source for independent reading on a wide assembly of topics, and as a window to learning the latest developments in contemporary topics. The book is unique in its detailed coverage of fundamental topics such as central limit theorems in numerous setups, likelihood based methods, goodness of fit, higher order asymptotics, as well as of the most modern topics such as the bootstrap, dependent data, Bayesian asymptotics, nonparametric density estimation, mixture models, and multiple testing and false discovery. It provides extensive bibliographic references on all topics that include very recent publications.
Anirban DasGupta is Professor of Statistics at Purdue University. He has also taught at the Wharton School of the University of Pennsylvania, at Cornell University, and at the University of California at San Diego. He has been on the editorial board of the Annals of Statistics since 1998 and has also served on the editorial boards of the Journal of the American Statistical Association, International Statistical Review, and the Journal of Statistical Planning and Inference. He has edited two monographs in the lecture notes monograph series of the Institute of Mathematical Statistics, is a Fellow of the Institute of Mathematical Statistics and has 70 refereed publications on theoretical statistics and probability in major journals.
"About the title" may belong to another edition of this title.
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