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Mathematical Statistics: Asymptotic Minimax Theory
 
Alexander Korostelev Wayne State University, Detroit, MI
Olga Korosteleva California State University, Long Beach, CA
Front Cover for Mathematical Statistics
Available Formats:
Hardcover ISBN: 978-0-8218-5283-5
Product Code: GSM/119
List Price: $72.00
MAA Member Price: $64.80
AMS Member Price: $57.60
Electronic ISBN: 978-1-4704-1593-8
Product Code: GSM/119.E
List Price: $67.00
MAA Member Price: $60.30
AMS Member Price: $53.60
Bundle Print and Electronic Formats and Save!
This product is available for purchase as a bundle. Purchasing as a bundle enables you to save on the electronic version.
List Price: $108.00
MAA Member Price: $97.20
AMS Member Price: $86.40
Front Cover for Mathematical Statistics
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  • Front Cover for Mathematical Statistics
  • Back Cover for Mathematical Statistics
Mathematical Statistics: Asymptotic Minimax Theory
Alexander Korostelev Wayne State University, Detroit, MI
Olga Korosteleva California State University, Long Beach, CA
Available Formats:
Hardcover ISBN:  978-0-8218-5283-5
Product Code:  GSM/119
List Price: $72.00
MAA Member Price: $64.80
AMS Member Price: $57.60
Electronic ISBN:  978-1-4704-1593-8
Product Code:  GSM/119.E
List Price: $67.00
MAA Member Price: $60.30
AMS Member Price: $53.60
Bundle Print and Electronic Formats and Save!
This product is available for purchase as a bundle. Purchasing as a bundle enables you to save on the electronic version.
List Price: $108.00
MAA Member Price: $97.20
AMS Member Price: $86.40
  • Book Details
     
     
    Graduate Studies in Mathematics
    Volume: 1192011; 246 pp
    MSC: Primary 62;

    This book is designed to bridge the gap between traditional textbooks in statistics and more advanced books that include the sophisticated nonparametric techniques. It covers topics in parametric and nonparametric large-sample estimation theory. The exposition is based on a collection of relatively simple statistical models. It gives a thorough mathematical analysis for each of them with all the rigorous proofs and explanations. The book also includes a number of helpful exercises.

    Prerequisites for the book include senior undergraduate/beginning graduate-level courses in probability and statistics.

    Readership

    Graduate students and research mathematicians interested in mathematical statistics.

  • Table of Contents
     
     
    • Part 1. Parametric models
    • Chapter 1. The Fisher efficiency
    • Chapter 2. The Bayes and minimax estimators
    • Chapter 3. Asymptotic minimaxity
    • Chapter 4. Some irregular statistical experiments
    • Chapter 5. Change-point problem
    • Chapter 6. Sequential estimators
    • Chapter 7. Linear parametric regression
    • Part 2. Nonparametric regression
    • Chapter 8. Estimation in nonparametric regression
    • Chapter 9. Local polynomial approximation of regression function
    • Chapter 10. Estimation of regression in global norms
    • Chapter 11. Estimation by splines
    • Chapter 12. Asymptotic optimality in global norms
    • Part 3. Estimation in nonparametric models
    • Chapter 13. Estimation of functionals
    • Chapter 14. Dimension and structure in nonparametric regression
    • Chapter 15. Adaptive estimation
    • Chapter 16. Testing of nonparametric hypotheses
  • Reviews
     
     
    • This is a well written book and should be of great interest to advanced graduate students/researchers in mathematical statistics. The material is presented with great clarity by using simple models as opposed to complex ones. ... Overall it should be of great value to advanced graduate students and researchers in theoretical statistics. The book can be recommended for libraries on campuses with a graduate program in statistics.

      Mathematical Reviews
  • Request Review Copy
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Volume: 1192011; 246 pp
MSC: Primary 62;

This book is designed to bridge the gap between traditional textbooks in statistics and more advanced books that include the sophisticated nonparametric techniques. It covers topics in parametric and nonparametric large-sample estimation theory. The exposition is based on a collection of relatively simple statistical models. It gives a thorough mathematical analysis for each of them with all the rigorous proofs and explanations. The book also includes a number of helpful exercises.

Prerequisites for the book include senior undergraduate/beginning graduate-level courses in probability and statistics.

Readership

Graduate students and research mathematicians interested in mathematical statistics.

  • Part 1. Parametric models
  • Chapter 1. The Fisher efficiency
  • Chapter 2. The Bayes and minimax estimators
  • Chapter 3. Asymptotic minimaxity
  • Chapter 4. Some irregular statistical experiments
  • Chapter 5. Change-point problem
  • Chapter 6. Sequential estimators
  • Chapter 7. Linear parametric regression
  • Part 2. Nonparametric regression
  • Chapter 8. Estimation in nonparametric regression
  • Chapter 9. Local polynomial approximation of regression function
  • Chapter 10. Estimation of regression in global norms
  • Chapter 11. Estimation by splines
  • Chapter 12. Asymptotic optimality in global norms
  • Part 3. Estimation in nonparametric models
  • Chapter 13. Estimation of functionals
  • Chapter 14. Dimension and structure in nonparametric regression
  • Chapter 15. Adaptive estimation
  • Chapter 16. Testing of nonparametric hypotheses
  • This is a well written book and should be of great interest to advanced graduate students/researchers in mathematical statistics. The material is presented with great clarity by using simple models as opposed to complex ones. ... Overall it should be of great value to advanced graduate students and researchers in theoretical statistics. The book can be recommended for libraries on campuses with a graduate program in statistics.

    Mathematical Reviews
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