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, nor are there 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 $L_1$-risk consistent. 
Daniel J. McDonald, Cosma Rohilla Shalizi, Mark Schervish (Carnegie   Mellon University)
stat.ML cs.LG math.PR