Limit theorems for functionals of mixing processes with applications to -statistics and dimension estimation |
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Authors: | Svetlana Borovkova Robert Burton Herold Dehling |
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Affiliation: | ITS-SSOR, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands ; Department of Mathematics, Oregon State University, Kidder Hall 368, Corvallis Oregon 97331 ; Fakultät für Mathematik, Ruhr-Universität Bochum, Universitätsstraße 150, D-44780 Bochum, Germany |
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Abstract: | In this paper we develop a general approach for investigating the asymptotic distribution of functionals of absolutely regular stochastic processes . Such functionals occur naturally as orbits of chaotic dynamical systems, and thus our results can be used to study probabilistic aspects of dynamical systems. We first prove some moment inequalities that are analogous to those for mixing sequences. With their help, several limit theorems can be proved in a rather straightforward manner. We illustrate this by re-proving a central limit theorem of Ibragimov and Linnik. Then we apply our techniques to -statistics
with symmetric kernel . We prove a law of large numbers, extending results of Aaronson, Burton, Dehling, Gilat, Hill and Weiss for absolutely regular processes. We also prove a central limit theorem under a different set of conditions than the known results of Denker and Keller. As our main application, we establish an invariance principle for -processes , indexed by some class of functions. We finally apply these results to study the asymptotic distribution of estimators of the fractal dimension of the attractor of a dynamical system. |
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