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Asymptotic Statistics

Asymptotic Statistics

$60.00

Part of Cambridge Series in Statistical and Probabilistic Mathematics

  • Date Published: June 2000
  • availability: Available
  • format: Paperback
  • isbn: 9780521784504

$60.00
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About the Authors
  • 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.

    • Based on notes from graduate and master's level courses taught by the author in Europe and in the US
    • Mathematically rigorous yet practical
    • Coverage of a wide range of classical and recent topics
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    Product details

    • Date Published: June 2000
    • format: Paperback
    • isbn: 9780521784504
    • length: 462 pages
    • dimensions: 254 x 178 x 24 mm
    • weight: 0.8kg
    • contains: 14 b/w illus. 8 tables
    • availability: Available
  • 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.

  • Author

    A. W. van der Vaart, Vrije Universiteit, Amsterdam

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