By István Berkes, Lajos Horváth, Johannes Schauer (auth.), Paul Doukhan, Gabriel Lang, Donatas Surgailis, Gilles Teyssière (eds.)
This quantity collects contemporary works on weakly based, long-memory and multifractal techniques and introduces new dependence measures for learning advanced stochastic platforms. different subject matters comprise the statistical conception for bootstrap and permutation statistics for endless variance approaches, the dependence constitution of max-stable procedures, and the statistical homes of spectral estimators of the lengthy reminiscence parameter. The asymptotic habit of Fejér graph integrals and their use for proving important restrict theorems for tapered estimators are investigated. New multifractal strategies are brought and their multifractal houses analyzed. Wavelet-based tools are used to review multifractal strategies with assorted multiresolution amounts, and to become aware of adjustments within the variance of random procedures. Linear regression types with long-range established mistakes are studied, as is the difficulty of detecting alterations of their parameters.