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Central limit theorems for dependent variables, II
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  • Published: September 1987

Central limit theorems for dependent variables, II

  • C. S. Withers1 

Probability Theory and Related Fields volume 76, pages 1–13 (1987)Cite this article

Summary

Two families of measures of the dependence between two random variables (rv's) are introduced. They include the strong-mixing ‘distance’. Two Central Limit Theorems (CLT's) are proved for dependent samples or processes where the dependence of the ‘past’ is not too strong. Tightness of the empirical process is shown to hold under conditions involving only the four-dimensional marginals of the sample.

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Authors and Affiliations

  1. Applied Mathematics Division, D.S.I.R., P.O.Box 1335, Wellington, New Zealand

    C. S. Withers

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  1. C. S. Withers
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Withers, C.S. Central limit theorems for dependent variables, II. Probab. Th. Rel. Fields 76, 1–13 (1987). https://doi.org/10.1007/BF00390273

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  • Received: 10 April 1984

  • Revised: 02 April 1987

  • Issue Date: September 1987

  • DOI: https://doi.org/10.1007/BF00390273

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Keywords

  • Stochastic Process
  • Probability Theory
  • Limit Theorem
  • Mathematical Biology
  • Central Limit
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