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arXiv.org e-Print Archive
Preprint . 2011
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Estimating beta-mixing coefficients.

descriptionPublicationkeyboard_double_arrow_right Article , Preprint , Conference object 04 Mar 2011 English Journal:JMLR workshop and conference proceedings, volume 15 (issn: 1938-7288, Copyright policy )
Authors: Daniel J. McDonald; Cosma Rohilla Shalizi; Mark J. Schervish;

pmid: 26279742

pmc: PMC4537076

arXiv: 1103.0941

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 $L_1$-risk consistent.

Comment: 9 pages, accepted by AIStats. CMU Statistics Technical Report

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Keywords

Computer Science - Machine Learning, Statistics - Machine Learning, Mathematics - Probability

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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