A Sequential Empirical Central Limit Theorem for Multiple Mixing Processes with Application to B-Geometrically Ergodic Markov Chains
arXiv:1303.4537 · doi:10.1214/EJP.v19-3216
Abstract
We investigate the convergence in distribution of sequential empirical processes of dependent data indexed by a class of functions F. Our technique is suitable for processes that satisfy a multiple mixing condition on a space of functions which differs from the class F. This situation occurs in the case of data arising from dynamical systems or Markov chains, for which the Perron--Frobenius or Markov operator, respectively, has a spectral gap on a restricted space. We provide applications to iterative Lipschitz models that contract on average.
Also available on http://ejp.ejpecp.org/article/view/3216. Note that the content of this version is identical to the one publisheb by "Electronic Journal of Probability". However, due to the use of different LaTeX-classes, the page number may differ
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