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Central Limit Theorems for Dependent Sequences

  • Anirban DasGupta
Part of the Springer Texts in Statistics book series (STS)

Keywords

Central Limit Theorem Dependent Sequence Martingale Property Large Sample Theory Stationary Gaussian Sequence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Billingsley, P. (1995). Probability and Measure, 3rd ed., John Wiley, New York.zbMATHGoogle Scholar
  2. Doob, J.L. (1971). What is a martingale?, Am. Math. Monthly, 78, 451–463.zbMATHCrossRefMathSciNetGoogle Scholar
  3. Ferguson, T.S. (1996). A Course in Large Sample Theory, Chapman and Hall, London.zbMATHGoogle Scholar
  4. Hall, P. and Heyde, C. (1980). Martingale Limit Theory and Its Application, Academic Press, New York.zbMATHGoogle Scholar
  5. Heyde, C. (1972). Martingales: a case for a place in the statistician’s repertoire, Aust. J. Stat., 14, 1–9.zbMATHMathSciNetCrossRefGoogle Scholar
  6. Hoeffding, W. and Robbins, H. (1948). The central limit theorem for dependent random variables, Duke Math. J., 15, 773–780.zbMATHCrossRefMathSciNetGoogle Scholar
  7. Lehmann, E.L. (1999). Elements of Large Sample Theory, Springer, New York.zbMATHGoogle Scholar
  8. Sen, P.K. and Singer, J. (1993). Large Sample Methods in Statistics: An Introduction with Applications, Chapman and Hall, New York.zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Anirban DasGupta
    • 1
  1. 1.Department of StatisticsPurdue UniversityWest Lafayette

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