Central Limit Theorems

  • Yuan Shih Chow
  • Henry Teicher
Part of the Springer Texts in Statistics book series (STS)


Central limit theorems have played a paramount role in probability theory starting—in the case of independent random variables—with the DeMoivreLaplace version and culminating with that of Lindeberg-Feller. The term “central” refers to the pervasive, although nonunique, role of the normal distribution as a limit of d.f.s of normalized sums of (classically independent) random variables. Central limit theorems also govern various classes of dependent random variables and the cases of martingales and interchangeable random variables will be considered.


Central Limit Theorem Independent Component Independent Random Variable Asymptotic Normality Double 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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Copyright information

© Springer Science+Business Media New York 1997

Authors and Affiliations

  • Yuan Shih Chow
    • 1
  • Henry Teicher
    • 2
  1. 1.Department of StatisticsColumbia UniversityNew YorkUSA
  2. 2.Department of StatisticsRutgers UniversityNew BrunswickUSA

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