Self-normalized central limit theorem for sums of weakly dependent random variables
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Publication:1322910
DOI10.1007/BF02214272zbMath0799.60020MaRDI QIDQ1322910
Publication date: 17 November 1994
Published in: Journal of Theoretical Probability (Search for Journal in Brave)
asymptotic normalitystrong consistencyweakly dependentstrictly stationary sequenceiterated logarithm type resultsrate of convergence for strong mixing
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Central limit and other weak theorems (60F05)
Related Items (5)
Gaussian approximation theorems for urn models and their applications ⋮ A Self-Normalized Central Limit Theorem for Markov Random Walks ⋮ Estimation of variance of partial sums of an associated sequence of random variables ⋮ Sensitivity of risk measures with respect to the normal approximation of total claim distributions ⋮ Testing that marginal sequences of data are not independent via self-normalization
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- An invariance principle for the law of the iterated logarithm
- Some Concepts of Dependence
- The Invariance Principle for Stationary Processes
- Functional central limit theorems for strictly stationary processes satisfying the strong mixing condition
- Some Limit Theorems for Stationary Processes
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