Dependence in Probability and Statistics

This volume provides an account on recent works on weakly dependent, long memory and multifractal processes. It introduces new dependence measures for studying complex stochastic systems. Others topics include the statistical theory for bootstrap and permutation statistics for infinite variance processes, the dependence structure of max-stable processes, and the statistical properties of spectral estimators of the long memory parameter. The asymptotic behavior of Fejer graph integrals and their use for proving central limit theorems for tapered estimators are investigated. New multifractal processes are introduced, and their multifractal properties are analysed. Wavelets-based methods are used for studying multifractal processes with different multiresolution quantities, and for detecting changes in the variance of random processes. Linear regression models with long range dependent errors are studied, as well as the issue of detection of changes in their parameters.