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- W2746105280 abstract "In this contribution, we extend the methodology proposed in Abry and Didier (2017) to obtain the first joint estimator of the real parts of the Hurst eigenvalues of $n$-variate OFBM. The procedure consists of a wavelet regression on the log-eigenvalues of the sample wavelet spectrum. The estimator is shown to be consistent for any time reversible OFBM and, under stronger assumptions, also asymptotically normal starting from either continuous or discrete time measurements. Simulation studies establish the finite sample effectiveness of the methodology and illustrate its benefits compared to univariate-like (entrywise) analysis. As an application, we revisit the well-known self-similar character of Internet traffic by applying the proposed methodology to 4-variate time series of modern, high quality Internet traffic data. The analysis reveals the presence of a rich multivariate self-similarity structure." @default.
- W2746105280 created "2017-08-31" @default.
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- W2746105280 date "2017-08-10" @default.
- W2746105280 modified "2023-09-25" @default.
- W2746105280 title "Wavelet eigenvalue regression for $n$-variate operator fractional Brownian motion" @default.
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