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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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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DOI: https://doi.org/10.1007/BF00390273
Keywords
- Stochastic Process
- Probability Theory
- Limit Theorem
- Mathematical Biology
- Central Limit