Estimating beta-mixing coefficients.
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.
Department of Statististics, Carnegie Mellon University, Pittsburgh, PA 15213, danielmc@stat.cmu.edu.
Daniel J
DJ
McDonald
Department of Statististics, Carnegie Mellon University, Pittsburgh, PA 15213, cshalizi@stat.cmu.edu.
Cosma Rohilla
CR
Shalizi
Department of Statististics, Carnegie Mellon University, Pittsburgh, PA 15213, mark@cmu.edu.
Mark
M
Schervish