Dependence in Probability and Statistics
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Beschreibung
This volume collects recent works on weakly dependent, long-memory and multifractal processes and introduces new dependence measures for studying complex stochastic systems. Other 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 Fejér graph integrals and their use for proving central limit theorems for tapered estimators are investigated. New multifractal processes are introduced and their multifractal properties analyzed. Wavelet-based methods are used to study multifractal processes with different multiresolution quantities, and to detect changes in the variance of random processes. Linear regression models with long-range dependent errors are studied, as is the issue of detecting changes in their parameters.
This volume provides the reader with a comprehensive recent account on dependent stochastic processes This book is a reference book for theoretical works, and provides some results that are of straight practical interest for the applied statistician/econometrician Includes supplementary material: sn.pub/extras
Autor*in
Paul Doukhan
Themen in »Dependence in Probability and Statistics«
Estimator Measure STATISTICA bootstrap change-point detection linear regression long memory multifractality wavelets weak dependence