Estimating beta-mixing coefficients

Daniel McDonald, Cosma Shalizi, Mark Schervish
; Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, JMLR Workshop and Conference Proceedings 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]

Cite this Paper


BibTeX
@InProceedings{pmlr-v15-mcdonald11a, title = {Estimating beta-mixing coefficients}, author = {Daniel McDonald and Cosma Shalizi and Mark Schervish}, booktitle = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics}, pages = {516--524}, year = {2011}, editor = {Geoffrey Gordon and David Dunson and Miroslav Dudík}, volume = {15}, series = {Proceedings of Machine Learning Research}, address = {Fort Lauderdale, FL, USA}, month = {11--13 Apr}, publisher = {JMLR Workshop and Conference Proceedings}, pdf = {http://proceedings.mlr.press/v15/mcdonald11a/mcdonald11a.pdf}, url = {http://proceedings.mlr.press/v15/mcdonald11a.html}, 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]} }
Endnote
%0 Conference Paper %T Estimating beta-mixing coefficients %A Daniel McDonald %A Cosma Shalizi %A Mark Schervish %B Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2011 %E Geoffrey Gordon %E David Dunson %E Miroslav Dudík %F pmlr-v15-mcdonald11a %I PMLR %J Proceedings of Machine Learning Research %P 516--524 %U http://proceedings.mlr.press %V 15 %W PMLR %X 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]
RIS
TY - CPAPER TI - Estimating beta-mixing coefficients AU - Daniel McDonald AU - Cosma Shalizi AU - Mark Schervish BT - Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics PY - 2011/06/14 DA - 2011/06/14 ED - Geoffrey Gordon ED - David Dunson ED - Miroslav Dudík ID - pmlr-v15-mcdonald11a PB - PMLR SP - 516 DP - PMLR EP - 524 L1 - http://proceedings.mlr.press/v15/mcdonald11a/mcdonald11a.pdf UR - http://proceedings.mlr.press/v15/mcdonald11a.html AB - 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] ER -
APA
McDonald, D., Shalizi, C. & Schervish, M.. (2011). Estimating beta-mixing coefficients. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, in PMLR 15:516-524

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