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The Resource A course in large sample theory, Thomas S. Ferguson

A course in large sample theory, Thomas S. Ferguson

Label
A course in large sample theory
Title
A course in large sample theory
Statement of responsibility
Thomas S. Ferguson
Creator
Subject
Language
eng
Member of
Cataloging source
DLC
http://library.link/vocab/creatorDate
1929-
http://library.link/vocab/creatorName
Ferguson, Thomas S.
Dewey number
519.5/2
Index
index present
LC call number
QA276.6
LC item number
.F466 1996
Literary form
non fiction
Nature of contents
bibliography
Series statement
Chapman & Hall texts in statistical science
http://library.link/vocab/subjectName
  • Sampling (Statistics)
  • Asymptotic distribution (Probability theory)
  • Law of large numbers
  • Sampling (Statistics)
  • Asymptotic distribution (Probability theory)
  • Law of large numbers
Label
A course in large sample theory, Thomas S. Ferguson
Instantiates
Publication
Bibliography note
Includes bibliographical references (pages 236-237) and index
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
pt. 1. Basic Probability. 1. Modes of Convergence. 2. Partial Converses to Theorem 1. 3. Convergence in Law. 4. Laws of Large Numbers. 5. Central Limit Theorems -- pt. 2. Basic Statistical Large Sample Theory. 6. Slutsky Theorems. 7. Functions of the Sample Moments. 8. The Sample Correlation Coefficient. 9. Pearson's Chi-Square. 10. Asymptotic Power of the Pearson Chi-Square Test -- pt. 3. Special Topics. 11. Stationary m-Dependent Sequences. 12. Some Rank Statistics. 13. Asymptotic Distribution of Sample Quantiles. 14. Asymptotic Theory of Extreme Order Statistics. 15. Asymptotic Joint Distributions of Extrema -- pt. 4. Efficient Estimation and Testing. 16. A Uniform Strong Law of Large Numbers. 17. Strong Consistency of Maximum-Likelihood Estimates. 18. Asymptotic Normality of the Maximum-Likelihood Estimate. 19. The Cramer-Rao Lower Bound. 20. Asymptotic Efficiency. 21. Asymptotic Normality of Posterior Distributions
Control code
36104352
Dimensions
24 cm
Edition
1st ed.
Extent
ix, 245 pages
Isbn
9780412043710
Isbn Type
(pbk. : acid-free paper)
Lccn
96086138
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Label
A course in large sample theory, Thomas S. Ferguson
Publication
Bibliography note
Includes bibliographical references (pages 236-237) and index
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
pt. 1. Basic Probability. 1. Modes of Convergence. 2. Partial Converses to Theorem 1. 3. Convergence in Law. 4. Laws of Large Numbers. 5. Central Limit Theorems -- pt. 2. Basic Statistical Large Sample Theory. 6. Slutsky Theorems. 7. Functions of the Sample Moments. 8. The Sample Correlation Coefficient. 9. Pearson's Chi-Square. 10. Asymptotic Power of the Pearson Chi-Square Test -- pt. 3. Special Topics. 11. Stationary m-Dependent Sequences. 12. Some Rank Statistics. 13. Asymptotic Distribution of Sample Quantiles. 14. Asymptotic Theory of Extreme Order Statistics. 15. Asymptotic Joint Distributions of Extrema -- pt. 4. Efficient Estimation and Testing. 16. A Uniform Strong Law of Large Numbers. 17. Strong Consistency of Maximum-Likelihood Estimates. 18. Asymptotic Normality of the Maximum-Likelihood Estimate. 19. The Cramer-Rao Lower Bound. 20. Asymptotic Efficiency. 21. Asymptotic Normality of Posterior Distributions
Control code
36104352
Dimensions
24 cm
Edition
1st ed.
Extent
ix, 245 pages
Isbn
9780412043710
Isbn Type
(pbk. : acid-free paper)
Lccn
96086138
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n

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