A functional limit theorem for self-normalized linear processes with random coefficients and i.i.d. heavy-tailed innovations
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Publication:6054051
DOI10.1007/s10986-023-09601-3zbMath1526.60029OpenAlexW4383551778WikidataQ123111483 ScholiaQ123111483MaRDI QIDQ6054051
Publication date: 24 October 2023
Published in: Lithuanian Mathematical Journal (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10986-023-09601-3
regular variationlinear processfunctional limit theoremself-normalized processSkorohod \(M_2\) topology
Special processes (60K99) Stable stochastic processes (60G52) Functional limit theorems; invariance principles (60F17) Rate of growth of functions, orders of infinity, slowly varying functions (26A12)
Cites Work
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- Functional weak convergence of partial maxima processes
- Functional convergence of linear processes with heavy-tailed innovations
- Self-normalized limit theorems: a survey
- A complete convergence theorem for stationary regularly varying multivariate time series
- Functional central limit theorems for self-normalized partial sums of linear processes
- A functional limit theorem for dependent sequences with infinite variance stable limits
- Limit theorems for sums of linearly generated random variables
- Limit theorems for self-normalized linear processes
- Functional limit theorems for linear processes in the domain of attraction of stable laws
- Pseudo-maximization and self-normalized processes
- Convergence to Lévy stable processes under some weak dependence conditions
- Tail probabilities for infinite series of regularly varying random vectors
- Regularly varying multivariate time series
- Extremes and related properties of random sequences and processes
- Extremal theory for stochastic processes
- Donsker's theorem for self-normalized partial sums processes
- Joint functional convergence of partial sums and maxima for linear processes
- Stable limits for partial sums of dependent random variables
- Point process and partial sum convergence for weakly dependent random variables with infinite variance
- A functional limit theorem for moving averages with weakly dependent heavy-tailed innovations
- A limit theorem for moving averages in the \(\alpha\)-stable domain of attraction
- Self-normalized limit theorems for linear processes generated by \(\rho\)-mixing innovations
- Stochastic-Process Limits
- ASYMPTOTIC PROPERTIES OF SELF-NORMALIZED LINEAR PROCESSES WITH LONG MEMORY
- Weak invariance principle for mixing sequences in the domain of attraction of normal law
- SELF-NORMALIZED WEAK LIMIT THEOREMS FOR A ø-MIXING SEQUENCE
- UNIT ROOT TESTING FOR FUNCTIONALS OF LINEAR PROCESSES
- Foundations of Modern Probability
- Heavy-Tailed Time Series
- Heavy-Tail Phenomena
- Limit Theorems for Moving Averages with Random Coefficients and Heavy-Tailed Noise
- Functional convergence for moving averages with heavy tails and random coefficients
