Limit theory for bilinear processes with heavy-tailed noise
DOI10.1214/aoap/1035463328zbMath0879.60053OpenAlexW1973940563MaRDI QIDQ1354837
Richard A. Davis, Sidney I. Resnick
Publication date: 22 January 1998
Published in: The Annals of Applied Probability (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1214/aoap/1035463328
Infinitely divisible distributions; stable distributions (60E07) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Extreme value theory; extremal stochastic processes (60G70) Functional limit theorems; invariance principles (60F17) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)
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Cites Work
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- Limit theory for moving averages of random variables with regularly varying tail probabilities
- More limit theory for the sample correlation function of moving averages
- Limit theory for the sample covariance and correlation functions of moving averages
- Time series: theory and methods.
- Limit distributions for linear programming time series estimators
- Parameter estimation for moving averages with positive innovations
- Point process and partial sum convergence for weakly dependent random variables with infinite variance
- Pitfalls of fitting autoregressive models for heavy-tailed time series
- ON THE EXISTENCE OF A GENERAL MULTIPLE BILINEAR TIME SERIES
- Point processes, regular variation and weak convergence
- Testing for independence in heavy tailed and positive innovation time series
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