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Almost sure convergence of recursive density estimators for stationary mixing processes

Author

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  • Masry, Elias

Abstract

Let {Xj} be a vector-valued stationary strong mixing process with a first-order probability density f on Rd. We establish rates of almost sure convergence for recursive density estimators n (x) of f(x) of the kernel type.

Suggested Citation

  • Masry, Elias, 1987. "Almost sure convergence of recursive density estimators for stationary mixing processes," Statistics & Probability Letters, Elsevier, vol. 5(4), pages 249-254, June.
    • Handle: RePEc:eee:stapro:v:5:y:1987:i:4:p:249-254
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    Cited by:

    1. P. Cattiaux & José R. León & C. Prieur, 2015. "Recursive estimation for stochastic damping hamiltonian systems," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 27(3), pages 401-424, September.
    2. Xin Wang, 2017. "Online Kernel estimation of stationary stochastic diffusion models," Quantitative Finance, Taylor & Francis Journals, vol. 17(7), pages 1089-1103, July.
    3. Juan Vilar-Fernández & José Vilar-Fernández, 2000. "Recursive local polynomial regression under dependence conditions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 9(1), pages 209-232, June.
    4. Lanh Tran, 1990. "Recursive kernel density estimators under a weak dependence condition," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 42(2), pages 305-329, June.

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