Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-19T04:33:52.876Z Has data issue: false hasContentIssue false

Mixing rates for potentials of non-summable variations

Published online by Cambridge University Press:  16 July 2021

CHRISTOPHE GALLESCO*
Affiliation:
Departmento de Estatística, Instituto de Matemática, Estatística e Ciência de Computação, Universidade de Campinas, Campinas, Brasil
DANIEL Y. TAKAHASHI
Affiliation:
Instituto do Cérebro, Universidade Federal do Rio Grande do Norte, Natal, Brasil (e-mail: [email protected])

Abstract

Mixing rates, relaxation rates, and decay of correlations for dynamics defined by potentials with summable variations are well understood, but little is known for non-summable variations. This paper exhibits upper bounds for these quantities for dynamics defined by potentials with square-summable variations. We obtain these bounds as corollaries of a new block coupling inequality between pairs of dynamics starting with different histories. As applications of our results, we prove a new weak invariance principle and a Hoeffding-type inequality.

Type
Original Article
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bressaud, X., Fernández, R. and Galves, A.. Decay of correlations for non-Hölderian dynamics. A coupling approach. Electron. J. Probab. 4(3) (1999), 19 pages (electronic).CrossRefGoogle Scholar
Berger, N., Hoffman, C. and Sidoravicius, V.. Non-uniqueness for specifications in ${\ell}^{2+\epsilon }$ . Ergod. Th. & Dynam. Sys. 38(4) (2018), 13421352.CrossRefGoogle Scholar
Chazottes, J.-R., Collet, P., Külske, C. and Redig, F.. Concentration inequalities for random fields via coupling. Probab. Theory Related Fields 137(1–2) (2007), 201225.CrossRefGoogle Scholar
Comets, F., Fernández, R. and Ferrari, P. A.. Processes with long memory: regenerative construction and perfect simulation. Ann. Appl. Probab. 12(3) (2002), 921943.CrossRefGoogle Scholar
Chazottes, J.-R., Gallo, S. and Takahashi, D. Y.. Optimal Gaussian concentration bounds for stochastic chains of unbounded memory. Preprint, 2020, arXiv:2001.06633.Google Scholar
Coelho, Z. and Quas, A.. Criteria for $\bar{d}$ -continuity. Trans. Amer. Math. Soc. 350(8) (1998), 32573268.CrossRefGoogle Scholar
Cover, T. M. and Thomas, J. A.. Elements of Information Theory, 2nd edn. Wiley, Hoboken, NJ, 2006.Google Scholar
Doeblin, W. and Fortet, R.. Sur des chaînes à liaisons complètes. Bull. Soc. Math. France 65 (1937), 132148.CrossRefGoogle Scholar
Fernández, R. and Maillard, G.. Chains with complete connections: general theory, uniqueness, loss of memory and mixing properties. J. Stat. Phys. 118(3–4) (2005), 555588.CrossRefGoogle Scholar
Gallesco, C., Gallo, S. and Takahashi, D. Y.. Explicit estimates in the Bramson–Kalikow model. Nonlinearity 27(9) (2014), 22812296.Google Scholar
Gallesco, C., Gallo, S. and Takahashi, D. Y.. Dynamic uniqueness for stochastic chains with unbounded memory. Stochastic Process. Appl. 128(2) (2018), 689706.CrossRefGoogle Scholar
Giacomin, G.. Random Polymer Models. Imperial College Press, Singapore, 2007.CrossRefGoogle Scholar
Gouëzel, S.. Sharp polynomial estimates for the decay of correlations. Israel J. Math. 139(1) (2004), 2965.CrossRefGoogle Scholar
Harris, T. E.. On chains of infinite order. Pacific J. Math. 5 (1955), 707724.CrossRefGoogle Scholar
Iosifescu, M. and Grigorescu, Ş.. Dependence with Complete Connections and Its Applications (Cambridge Tracts in Mathematics, 96) . Cambridge University Press, Cambridge, 1990.Google Scholar
Johansson, A. and Öberg, A.. Square summability of variations and convergence of the transfer operator. Ergod. Th. & Dynam. Sys. 28(4) (2008), 11451151.CrossRefGoogle Scholar
Johansson, A., Öberg, A. and Pollicott, M.. Countable state shifts and uniqueness of g-measures. Amer. J. Math. 129(6) (2007), 15011511.CrossRefGoogle Scholar
Johansson, A., Öberg, A. and Pollicott, M.. Unique Bernoulli $g$ -measures. J. Eur. Math. Soc. 14(5) (2012), 15991615.CrossRefGoogle Scholar
Kalikow, S.. Random Markov processes and uniform martingales. Israel J. Math. 71(1) (1990), 3354.CrossRefGoogle Scholar
Keane, M.. Strongly mixing $g$ -measures. Invent. Math. 16(4) (1972), 309324.CrossRefGoogle Scholar
Kedem, B. and Fokianos, K.. Regression Models for Time Series Analysis (Wiley Series in Probability and Statistics, 488). John Wiley & Sons, New Jersey, 2005.Google Scholar
Lindvall, T.. On Strassen’s theorem on stochastic domination. Electron. Commun. Probab. 4 (1999), 5159.CrossRefGoogle Scholar
Marton, K.. Measure concentration for a class of random processes. Probab. Theory Related Fields 110(3) (1998), 427439.CrossRefGoogle Scholar
Pollicott, M.. Rates of mixing for potentials of summable variation. Trans. Amer. Math. Soc. 352(2) (2000), 843853.CrossRefGoogle Scholar
Reiss, R.-D.. Approximate Distributions of Order Statistics: With Applications to Nonparametric Statistics. Springer Science & Business Media, Springer-Verlag, New York, 2012.Google Scholar
Sason, I. and Verdú, S.. f-divergence inequalities. IEEE Trans. Inform. Theory 62(11) (2016), 59736006.CrossRefGoogle Scholar
Thorisson, H.. Coupling, Stationarity, and Regeneration (Probability and Its Applications) . Springer, New York, 2000.CrossRefGoogle Scholar
Tyran-Kamińska, M.. An invariance principle for maps with polynomial decay of correlations. Comm. Math. Phys. 260(1) (2005), 115.CrossRefGoogle Scholar
Walters, P.. Ruelle’s operator theorem and $g$ -measures. Trans. Amer. Math. Soc. 214 (1975), 375387.Google Scholar