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A Course in Large Sample Theory (Chapman & Hall/CRC Texts in Statistical Science) 1st Edition
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A Course in Large Sample Theory is presented in four parts. The first treats basic probabilistic notions, the second features the basic statistical tools for expanding the theory, the third contains special topics as applications of the general theory, and the fourth covers more standard statistical topics. Nearly all topics are covered in their multivariate setting.
The book is intended as a first year graduate course in large sample theory for statisticians. It has been used by graduate students in statistics, biostatistics, mathematics, and related fields. Throughout the book there are many examples and exercises with solutions. It is an ideal text for self study.
- ISBN-100412043718
- ISBN-13978-0412043710
- Edition1st
- Publication dateJanuary 1, 1996
- LanguageEnglish
- Dimensions6.25 x 0.59 x 9.25 inches
- Print length258 pages
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- Publisher : Chapman and Hall/CRC; 1st edition (January 1, 1996)
- Language : English
- Paperback : 258 pages
- ISBN-10 : 0412043718
- ISBN-13 : 978-0412043710
- Item Weight : 1.06 pounds
- Dimensions : 6.25 x 0.59 x 9.25 inches
- Best Sellers Rank: #1,886,734 in Books (See Top 100 in Books)
- #129 in Biostatistics (Books)
- #3,355 in Probability & Statistics (Books)
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For those who want to take this material on for self-study: Pick this book but... this level of this book (ie, the material) is comparable to real analysis but with more direct applications. That is, an individual will succeed in using this book for self-study if (and perhaps only if) she has a good base in analysis and proofs and feels comfortable adapting that knowledge to statistics. An individual with little or no background in analysis proofs will have a very difficult time using this book for self-study. That said, if you want to learn the material, this book would be a prime starting location. If you don't have a good background in analysis, consider spending some time preparing by running over the theory of limits before engaging this book.
For those who are taking a course and are using this book, be happy your professor picked it -- it's clear and concise. This is a book worth buying. Due to the level of the material, rereading chapters is sometimes necessary but is easily manageable since chapters are concise and include examples.
Regarding its coverage, the book is more elementary than other books such as Asymptotic Statistics by Var der Vaart and is also slightly outdated. A consequence is that some important modern results are missing, for example asymptotics of M estimators, non-parametrics/semi-parametric, local normality. On the other hand, in order to cover these additional topics the book would have to be much longer and contain more advanced math.
If you are learning this topic for the first time, I can't think of a better book to read. If, on the other hand, you have already learned asymptotic statistics in some form and wish to learn more advanced and modern material you should probably use a different book.



