Estimating beta-mixing coefficients

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Daniel McDonald, Cosma Shalizi, Mark Schervish ;
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, PMLR 15:516-524, 2011.

Abstract

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 L1-risk consistent. [pdf]

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