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Asymptotic behavior of ultimately contractive iterated random Lipschitz functions

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EN
Abstrakty
EN
Let (Fn)n≥0 be a random sequence of i.i.d. global Lipschitz functions on a complete separable metric space (X; d) with Lipschit constants L1; L2; : : : For n ≥0, denote by Mx n = Fn○ : : : ○ F1(x) and ^Mx n = Fn○ : : : ○ F1(x) the associated sequences of forward and backward iterations, respectively. If E log+ L1 < 0 (mean contraction) and E log+ d ( F1(x0); x0) is finite for some x0ЄX, then it is known (see [9]) that, for each x Є X, the Markov chain Mx n converges weakly to its unique stationary distribution π, while ^M xn is a.s. convergent to a random variable ^M∞ which does not depend on x and has distribution π. In [2], renewal theoretic methods have been successfully employed to provide convergence rate results for ^M x n, which then also lead to corresponding assertions for Mx n via Mx n d= ^M x n for all n and x, where d= means equality in law. Here our purpose is to demonstrate how these methods are extended to the more general situation where only ultimate contraction, i.e. an a.s. negative Lyapunov exponent limn→∞ n−1 log l(Fn○ : : : ○ F1) is assumed (here l(F) denotes the Lipschitz constant of F). This not only leads to an extension of the results from [2] but in fact also to improvements of the obtained convergence rate.
Rocznik
Strony
321--336
Opis fizyczny
Bibliogr. 14 poz.
Twórcy
autor
  • Institut für Mathematische Statistik, FB 10 Westfälische Wilhelms-Universität Münster, Einsteinstrasse 62, 48149 Münster, Germany
autor
  • Institut für Mathematische Statistik, FB 10 Westfälische Wilhelms-Universität Münster, Einsteinstrasse 62, 48149 Münster, Germany
Bibliografia
  • [1] G. Alsmeyer, On the Harris recurrence of iterated random Lipschitz functions and related convergence rate results, J. Theoret. Probab. 16 (2003), pp. 217-247.
  • [2] G. Alsmeyer and C. D. Fuh, Limit theorems for iterated random functions by regenerative methods, Stochastic Process. Appl. 96 (2001), pp. 123-142. Corrigendum: 97 (2002), pp. 341-345.
  • [3] L. Arnold and H. Crauel, Iterated function systems and multiplicative ergodic theory, in: Diffusion Processes and Related Problems in Analysis 2, M. Pinsky and V. Wihstutz (Eds.), Birkhäuser, Boston 1992, pp. 283-305.
  • [4] M. Babillot, P. Bougerol and L. Elie, The random difference equation Xn =AnXn−1 + Bn in the critical case, Ann. Probab. 25 (1997), pp. 478-493.
  • [5] M. F. Barnsley and J. H. Elton, A new class of Markov processes for image encoding, Adv. in Appl. Probab. 20 (1988), pp. 14-32.
  • [6] M. Benda, A central limit theorem for contractive dynamical systems, J. Appl. Probab. 35 (1998), pp. 200-205.
  • [7] Y. S. Chow and T. L. Lai, Some one-sided theorems on the tail distribution of sample sums with applications to the last time and largest excess of boundary crossings, Trans. Amer. Math. Soc. 208 (1975), pp. 51-72.
  • [8] P. Diaconis and D. Freedman, Iterated random functions, SIAM Rev. 41 (1999), pp. 45-76.
  • [9] J. H. Elton, A multiplicative ergodic theorem for Lipschitz maps, Stochatic Process. Appl. 34 (1990), pp. 39-47.
  • [10] A. Gut, Stopped Random Walks: Limit Theorems and Applications, Springer, New York 1988.
  • [11] J. E. Hutchinson, Fractals and self-similarity, Indiana Univ. Math. J. 30 (1981), pp. 713-747.
  • [12] G. Letac, A contraction principle for certain Markov chains and its applications, Contemp. Math. 50 (1986), pp. 263-273.
  • [13] D. Silvestrov and Ö. Stenflo, Ergodic theorems for iterated function systems controlled by regenerative sequences, J. Theoret. Probab. 11 (1998), pp. 589-608.
  • [14] D. Steinsaltz, Locally contractive iterated function systems, Ann. Probab. 27 (1999), pp. 1952-1979.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1b7e131f-ef4e-45e1-a9ca-fbafe681c5d0
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