Estimating Beta-Mixing Coefficients
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.
Cite
Text
McDonald et al. "Estimating Beta-Mixing Coefficients." Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011.Markdown
[McDonald et al. "Estimating Beta-Mixing Coefficients." Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011.](https://mlanthology.org/aistats/2011/mcdonald2011aistats-estimating/)BibTeX
@inproceedings{mcdonald2011aistats-estimating,
title = {{Estimating Beta-Mixing Coefficients}},
author = {McDonald, Daniel and Shalizi, Cosma and Schervish, Mark},
booktitle = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics},
year = {2011},
pages = {516-524},
volume = {15},
url = {https://mlanthology.org/aistats/2011/mcdonald2011aistats-estimating/}
}