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Central Limit Theorems

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

Abstract

Central limit theorems have played a paramount role in probability theory starting—in the case of independent random variables—with the DeMoivre- Laplace 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.

Keywords

Central Limit Theorem Independent Random Variable Asymptotic Normality Double Sequence Dependent Random Variable 
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-Verlag New York Inc. 1988

Authors and Affiliations

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

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