Estimating $\beta$-mixing coefficients
Machine Learning
2022-03-18 v1 Machine Learning
Probability
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 -mixing rate based on a single stationary sample path and show it is -risk consistent.
Cite
@article{arxiv.1103.0941,
title = {Estimating $\beta$-mixing coefficients},
author = {Daniel J. McDonald and Cosma Rohilla Shalizi and Mark Schervish},
journal= {arXiv preprint arXiv:1103.0941},
year = {2022}
}
Comments
9 pages, accepted by AIStats. CMU Statistics Technical Report