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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
Summary
A Course in Large Sample Theory is presented in four parts. The first treats basic probabilistic notions, the second features the basic statistical tools for expanding the theory, the third contains special topics as applications of the general theory, and the fourth covers more standard statistical topics. Nearly all topics are covered in their multivariate setting.The book is intended as a first year graduate course in large sample theory for statisticians. It has been used by graduate students in statistics, biostatistics, mathematics, and related fields. Throughout the book there are many examples and exercises with solutions. It is an ideal text for self study
Member of
http://library.link/vocab/creatorDate
1929-
http://library.link/vocab/creatorName
Ferguson, Thomas S.
Dewey number
519.52
Index
no index present
Literary form
non fiction
Nature of contents
dictionaries
Series statement
Chapman & Hall texts in statistical science
http://library.link/vocab/subjectName
  • Sampling (Statistics)
  • Asymptotic distribution (Probability theory)
  • Law of large numbers
  • Probability Theory
Label
A course in large sample theory, Thomas S. Ferguson
Instantiates
Publication
Copyright
Bibliography note
Includes bibliographical references and index
Contents
  • chapter 4 4 Laws of Large Numbers
  • Thomas S. Ferguson
  • chapter 5 5 Central Limit Theorems
  • Thomas S. Ferguson
  • part 2 Basic Statistical Large Sample Theory
  • Thomas S. Ferguson
  • chapter 6 Slutsky Theorems
  • Thomas S. Ferguson
  • chapter 7 Functions of the Sample Moments
  • Thomas S. Ferguson
  • Part 1 Basic Probability Theory
  • chapter 8 The Sample Correlation Coefficient
  • Thomas S. Ferguson
  • chapter 9 Pearson’s Chi-Square
  • Thomas S. Ferguson
  • chapter 10 Asymptotic Power of the Pearson Chi-Square Test
  • Thomas S. Ferguson
  • part 3 Special Topics
  • Thomas S. Ferguson
  • chapter 11 Stationary m-Dependent Sequences
  • Thomas S. Ferguson
  • Thomas S. Ferguson
  • chapter 12 Some Rank Statistics
  • Thomas S. Ferguson
  • chapter 13 Asymptotic Distribution of Sample Quantiles
  • Thomas S. Ferguson
  • chapter 14 Asymptotic Theory of Extreme Order Statistics*
  • Thomas S. Ferguson
  • chapter 15 Asymptotic Joint Distributions of Extrema
  • Thomas S. Ferguson
  • part 4 Efficient Estimation and Testing
  • Thomas S. Ferguson
  • chapter 1 Modes of Convergence
  • chapter 16 A Uniform Strong Law of Large Numbers
  • Thomas S. Ferguson
  • chapter 17 Strong Consistency of Maximum-Likelihood Estimates
  • Thomas S. Ferguson
  • chapter 18 Asymptotic Normality of the Maximum-Likelihood Estimate
  • Thomas S. Ferguson
  • chapter 19 The Cramér-Rao Lower Bound
  • Thomas S. Ferguson
  • chapter 20 Asymptotic Efficiency
  • Thomas S. Ferguson
  • Thomas S. Ferguson
  • chapter 21 Asymptotic Normality of Posterior Distributions
  • Thomas S. Ferguson
  • chapter 22 Asymptotic Distribution of the Likelihood Ratio Test Statistic
  • Thomas S. Ferguson
  • chapter 23 Minimum Chi-Square Estimates
  • Thomas S. Ferguson
  • chapter 24 24 General Chi-Square Tests
  • Thomas S. Ferguson
  • chapter 2 Partial Converses to Theorem 1
  • Thomas S. Ferguson
  • chapter 3 Convergence in Law
  • Thomas S. Ferguson
Control code
on1032029135
Dimensions
unknown
Extent
1 online resource
Form of item
online
Isbn
9781315136288
Note
Taylor & Francis
Specific material designation
remote
System control number
(OCoLC)1032029135
Label
A course in large sample theory, Thomas S. Ferguson
Publication
Copyright
Bibliography note
Includes bibliographical references and index
Contents
  • chapter 4 4 Laws of Large Numbers
  • Thomas S. Ferguson
  • chapter 5 5 Central Limit Theorems
  • Thomas S. Ferguson
  • part 2 Basic Statistical Large Sample Theory
  • Thomas S. Ferguson
  • chapter 6 Slutsky Theorems
  • Thomas S. Ferguson
  • chapter 7 Functions of the Sample Moments
  • Thomas S. Ferguson
  • Part 1 Basic Probability Theory
  • chapter 8 The Sample Correlation Coefficient
  • Thomas S. Ferguson
  • chapter 9 Pearson’s Chi-Square
  • Thomas S. Ferguson
  • chapter 10 Asymptotic Power of the Pearson Chi-Square Test
  • Thomas S. Ferguson
  • part 3 Special Topics
  • Thomas S. Ferguson
  • chapter 11 Stationary m-Dependent Sequences
  • Thomas S. Ferguson
  • Thomas S. Ferguson
  • chapter 12 Some Rank Statistics
  • Thomas S. Ferguson
  • chapter 13 Asymptotic Distribution of Sample Quantiles
  • Thomas S. Ferguson
  • chapter 14 Asymptotic Theory of Extreme Order Statistics*
  • Thomas S. Ferguson
  • chapter 15 Asymptotic Joint Distributions of Extrema
  • Thomas S. Ferguson
  • part 4 Efficient Estimation and Testing
  • Thomas S. Ferguson
  • chapter 1 Modes of Convergence
  • chapter 16 A Uniform Strong Law of Large Numbers
  • Thomas S. Ferguson
  • chapter 17 Strong Consistency of Maximum-Likelihood Estimates
  • Thomas S. Ferguson
  • chapter 18 Asymptotic Normality of the Maximum-Likelihood Estimate
  • Thomas S. Ferguson
  • chapter 19 The Cramér-Rao Lower Bound
  • Thomas S. Ferguson
  • chapter 20 Asymptotic Efficiency
  • Thomas S. Ferguson
  • Thomas S. Ferguson
  • chapter 21 Asymptotic Normality of Posterior Distributions
  • Thomas S. Ferguson
  • chapter 22 Asymptotic Distribution of the Likelihood Ratio Test Statistic
  • Thomas S. Ferguson
  • chapter 23 Minimum Chi-Square Estimates
  • Thomas S. Ferguson
  • chapter 24 24 General Chi-Square Tests
  • Thomas S. Ferguson
  • chapter 2 Partial Converses to Theorem 1
  • Thomas S. Ferguson
  • chapter 3 Convergence in Law
  • Thomas S. Ferguson
Control code
on1032029135
Dimensions
unknown
Extent
1 online resource
Form of item
online
Isbn
9781315136288
Note
Taylor & Francis
Specific material designation
remote
System control number
(OCoLC)1032029135

Library Locations

    • InternetBorrow it
      Albany, Auckland, 0632, NZ
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