Strong approximation for RCA(1) time series with applications
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Publication:1881237
DOI10.1016/j.spl.2004.04.007zbMath1086.62092OpenAlexW2081372596MaRDI QIDQ1881237
Publication date: 4 October 2004
Published in: Statistics \& Probability Letters (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.spl.2004.04.007
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Sequential statistical analysis (62L10) Functional limit theorems; invariance principles (60F17)
Related Items (10)
Strong approximations and sequential change-point analysis for diffusion processes ⋮ Changepoint Detection in Heteroscedastic Random Coefficient Autoregressive Models ⋮ Derivation of Kurtosis and Option Pricing Formulas for Popular Volatility Models with Applications in Finance ⋮ Properties of a new family of volatility sign models ⋮ Monitoring Variance Change in Infinite Order Moving Average Processes and Nonstationary Autoregressive Processes ⋮ Testing for parameter stability in \(RCA(1)\) time series ⋮ Monitoring parameter changes in RCA(\(p\)) models ⋮ RCA models with GARCH innovations ⋮ Monitoring Changes in RCA Models ⋮ Structural Change Monitoring for Random Coefficient Autoregressive Time Series
Cites Work
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- On strong invariance principles under dependence assumptions
- Almost sure invariance principles for partial sums of mixing B-valued random variables
- Detection of changes in linear sequences
- Delay time in sequential detection of change
- Monitoring changes in linear models
- RANDOM COEFFICIENT AUTOREGRESSIVE PROCESSES:A MARKOV CHAIN ANALYSIS OF STATIONARITY AND FINITENESS OF MOMENTS
- An approximation of partial sums of independent RV's, and the sample DF. II
- An approximation of partial sums of independent RV'-s, and the sample DF. I
- TESTING FOR THE RANDOMNESS OF AUTOREGRESSIVE COEFFICIENTS
- Monitoring Structural Change
- An invariance principle for the law of the iterated logarithm
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