Estimating beta-mixing coefficients

نویسندگان

  • Daniel J. McDonald
  • Cosma Rohilla Shalizi
  • Mark J. Schervish
چکیده

The literature on statistical learning for time series assumes the asymptotic independence or "mixing" of the data-generating process. These mixing assumptions are never tested, and there are no methods for estimating mixing rates from data. We give an estimator for the beta-mixing rate based on a single stationary sample path and show it is L1-risk consistent.

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عنوان ژورنال:
  • JMLR workshop and conference proceedings

دوره 15  شماره 

صفحات  -

تاریخ انتشار 2011