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Asymptotic Statistics

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Synopses & Reviews

Publisher Comments:

Here is a practical and mathematically rigorous introduction to the field of asymptotic statistics. In addition to most of the standard topics of an asymptotics course--likelihood inference, M-estimation, the theory of asymptotic efficiency, U-statistics, and rank procedures--the book also presents recent research topics such as semiparametric models, the bootstrap, and empirical processes and their applications. The topics are organized from the central idea of approximation by limit experiments, one of the book's unifying themes that mainly entails the local approximation of the classical i.i.d. set up with smooth parameters by location experiments involving a single, normally distributed observation.

Synopsis:

A mathematically rigorous, practical introduction presenting standard topics plus research.

Synopsis:

This mathematically rigorous, practical introduction to the field of asymptotic statistics develops most of the usual topics of an asymptotics course, and also presents recent research topics such as empirical processes, the bootstrap, and semiparametric models.

Synopsis:

This book is an introduction to the field of asymptotic statistics. The treatment is mathematically rigorous but practical rather than simply technical. The topics are organized from the central idea of approximation by limit experiments, which gives the book one of its unifying themes. In addition to the usual topics of an asymptotics course, the author also presents recent research topics such as semiparametric models, the bootstrap, and empirical processes. Suitable as a text for a graduate or Master's level statistics course, this book will also serve as a reference for researchers in statistics, probability, and their applications.

Table of Contents

1. Introduction; 2. Stochastic convergence; 3. The delta-method; 4. Moment estimators; 5. M- and Z-estimators; 6. Contiguity; 7. Local asymptotic normality; 8. Efficiency of estimators; 9. Limits of experiments; 10. Bayes procedures; 11. Projections; 12. U-statistics; 13. Rank, sign, and permutation statistics; 14. Relative efficiency of tests; 15. Efficiency of tests; 16. Likelihood ratio tests; 17. Chi-square tests; 18. Stochastic convergence in metric spaces; 19. Empirical processes; 20. The functional delta-method; 21. Quantiles and order statistics; 22. L-statistics; 23. The bootstrap; 24. Nonparametric density estimation; 25. Semiparametric models.

Product Details

ISBN:
9780521784504
Editor:
Gill, R.
Editor:
Ripley, B. D.
Editor:
Gill, R.
Editor:
Ripley, B. D.
Author:
Ripley, B. D.
Author:
Williams, D.
Author:
Ross, S.
Author:
Gill, R.
Author:
Van Der Vaart, A. W.
Author:
Stein, M.
Author:
Vaart, A. W. Van Der
Publisher:
Cambridge University Press
Location:
New York
Subject:
Statistics
Subject:
Mathematical statistics
Subject:
Asymptotic efficiencies (Statistics)
Subject:
Asymptotic efficiencies
Subject:
Probability & Statistics - General
Subject:
Asymptotic efficiencies (Statistics) Asymptot
Subject:
Mathematics | Probability and Statistics
Edition Description:
Trade paper
Series:
Cambridge Series in Statistical and Probabilistic Mathematic
Series Volume:
3
Publication Date:
20000131
Binding:
TRADE PAPER
Grade Level:
Professional and scholarly
Language:
English
Illustrations:
14 b/w illus. 8 tables
Pages:
462
Dimensions:
10.00 in.

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Science and Mathematics » Mathematics » Probability and Statistics » Statistics

Asymptotic Statistics New Trade Paper
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Product details 462 pages Cambridge University Press - English 9780521784504 Reviews:
"Synopsis" by , A mathematically rigorous, practical introduction presenting standard topics plus research.
"Synopsis" by , This mathematically rigorous, practical introduction to the field of asymptotic statistics develops most of the usual topics of an asymptotics course, and also presents recent research topics such as empirical processes, the bootstrap, and semiparametric models.
"Synopsis" by , This book is an introduction to the field of asymptotic statistics. The treatment is mathematically rigorous but practical rather than simply technical. The topics are organized from the central idea of approximation by limit experiments, which gives the book one of its unifying themes. In addition to the usual topics of an asymptotics course, the author also presents recent research topics such as semiparametric models, the bootstrap, and empirical processes. Suitable as a text for a graduate or Master's level statistics course, this book will also serve as a reference for researchers in statistics, probability, and their applications.
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