Meaningful MRA intitialization for discrete time series
Signal Processing - Special issue on current topics in adaptive filtering for hands-free acoustic communication and beyond
LASS: a tool for the local analysis of self-similarity
Computational Statistics & Data Analysis
A wavelet-based joint estimator of the parameters of long-range dependence
IEEE Transactions on Information Theory
A novel approach for distributed application scheduling based on prediction of communication events
Future Generation Computer Systems
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In order to estimate the Hurst exponent of long-range dependent time series numerous estimators such as based e.g. on rescaled range statistic (R/S) or detrended fluctuation analysis (DFA) are traditionally employed. Motivated by empirical behaviour of the bias of R/S estimator, its bias-corrected version is proposed. It has smaller mean squared error than DFA and behaves comparably to wavelet estimator for traces of size as large as 2^1^5 drawn from some commonly considered long-range dependent processes. It is also shown that several variants of R/S and DFA estimators are possible depending on the way they are defined and that they differ greatly in their performance.