A sequential empirical CLT for multiple mixing processes with application to B - geometrically ergodic Markov chains ∗
نویسندگان
چکیده
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.
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