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| Hardcover ISBN: | 978-0-8218-5283-5 |
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| Sale Price: | $119.60 $91.98 |
| Hardcover ISBN: | 978-0-8218-5283-5 |
| Product Code: | GSM/119 |
| List Price: | $99.00 |
| MAA Member Price: | $89.10 |
| AMS Member Price: | $79.20 |
| Sale Price: | $64.35 |
| eBook ISBN: | 978-1-4704-1593-8 |
| Product Code: | GSM/119.E |
| List Price: | $85.00 |
| MAA Member Price: | $76.50 |
| AMS Member Price: | $68.00 |
| Sale Price: | $55.25 |
| Hardcover ISBN: | 978-0-8218-5283-5 |
| eBook ISBN: | 978-1-4704-1593-8 |
| Product Code: | GSM/119.B |
| List Price: | $184.00 $141.50 |
| MAA Member Price: | $165.60 $127.35 |
| AMS Member Price: | $147.20 $113.20 |
| Sale Price: | $119.60 $91.98 |
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Book DetailsGraduate Studies in MathematicsVolume: 119; 2011; 246 ppMSC: 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.
ReadershipGraduate students and research mathematicians interested in mathematical statistics.
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Table of Contents
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Part 1. Parametric models
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Chapter 1. The Fisher efficiency
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Chapter 2. The Bayes and minimax estimators
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Chapter 3. Asymptotic minimaxity
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Chapter 4. Some irregular statistical experiments
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Chapter 5. Change-point problem
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Chapter 6. Sequential estimators
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Chapter 7. Linear parametric regression
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Part 2. Nonparametric regression
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Chapter 8. Estimation in nonparametric regression
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Chapter 9. Local polynomial approximation of regression function
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Chapter 10. Estimation of regression in global norms
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Chapter 11. Estimation by splines
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Chapter 12. Asymptotic optimality in global norms
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Part 3. Estimation in nonparametric models
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Chapter 13. Estimation of functionals
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Chapter 14. Dimension and structure in nonparametric regression
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Chapter 15. Adaptive estimation
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Chapter 16. Testing of nonparametric hypotheses
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Additional Material
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Reviews
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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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RequestsReview Copy – for publishers of book reviewsPermission – for use of book, eBook, or Journal contentAccessibility – to request an alternate format of an AMS title
- Book Details
- Table of Contents
- Additional Material
- Reviews
- Requests
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.
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
