Limit Theorems: Extensions and Generalizations

  • Allan Gut
Part of the Springer Texts in Statistics book series (STS, volume 75)


Let us recapitulate what we have learned so far. After an introductory chapter containing some set theory and measure theory, we met a chapter on random variables and expectations, the probabilistic equivalent of Lebesgue integration on finite measure spaces. This was then followed by a number of probabilistic tools and methods, and one chapter each on the three cornerstone results, the law of large numbers, the central limit theorem, and the law of the iterated logarithm—LLN, CLT, and LIL. So, what’s up next?


Central Limit Theorem Independent Random Variable Limit Distribution Stable Distribution Iterate Logarithm 
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Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.Department of MathematicsUppsala UniversityUppsalaSweden

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