The invariance principle for~conditional empirical processes
Izvestiya. Mathematics , Tome 69 (2005) no. 4, pp. 771-789.

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We prove the convergence of finite-dimensional distributions and establish density for Nadaraya–Watson conditional empirical processes. The observations are assumed to be described by a strictly stationary sequence of random variables whose mixing coefficients decay polynomially. The proof of density of such processes in the space of continuous functionals uses entropy conditions on the class of indexing functions.
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D. V. Poryvai. The invariance principle for~conditional empirical processes. Izvestiya. Mathematics , Tome 69 (2005) no. 4, pp. 771-789. http://geodesic.mathdoc.fr/item/IM2_2005_69_4_a5/

[1] Alexander K. S., “Probability inequalities for empirical processes and law of the iterated logarithm”, Annals of Probability, 12:4 (1984), 1041–1067 | DOI | MR | Zbl

[2] Bosq D., Nonparametric Statistics for Stochastics Processes, Lecture Notes in Statistics, 110, Springer-Verlag, N. Y., 1998 | MR | Zbl

[3] Dedecker J., “Exponential inequalities and functional central limit theorems for random fields”, ESAIM: Probab. Statist., 2001, no. 5, 77–104 | DOI | MR | Zbl

[4] Dedecker J., Rio E., “On the functional central limit theorem for stationary processes”, Annales de l'Institut Henri Poincaré Probabilitiés et Statistiques, 2000, no. 36, 1–34 | DOI | MR | Zbl

[5] Doukhan P., Mixing. Properties and Examples, Lecture Notes in Statistics, 85, Springer-Verlag, N. Y., 1994 | MR | Zbl

[6] Doukhan P., Massart P., Rio E., “Invariance principles for absolutely regular empirical processes”, Annales de l'Institut Henri Poincaré Probabilitiés et Statistiques, 1995, no. 31, 393–427 | MR | Zbl

[7] Fan J., Truong Y. K., “Nonparametric regression with errors in variables”, Ann. Statist., 1993, no. 21, 1900–1925 | DOI | MR | Zbl

[8] Franke J., Härdle W., Kreiss J.-P., Mercurio D., “Nonparametric Estimation in a Stochastic Volatility Model”, Recent Advances and Trends in Nonparametric Statistics, eds. M. G. Akritas, D. N. Politis, Elsevier, 2003, 303–312

[9] Parzen E., “On estimation of a probability density function and mode”, Ann. Math. Statist., 1962, no. 33, 1065–1076 | DOI | MR | Zbl

[10] Polonik W., Yao Q., “Set-indexed conditional empirical and quantile processes based on dependent data”, J. Multivar. Anal., 2002, no. 80, 234–255 | DOI | MR | Zbl

[11] Scott L. O., “Option pricing when the variance changes randomly: Theory, Estimation and Application”, J. Financial Quant. Anal., 1987, no. 22, 419–438 | DOI

[12] Taylor S. J., “Modelling Stochastic Volatility: A Review and Comparative Study”, Math. Finance, 1994, no. 4, 183–204 | DOI | Zbl

[13] Tuyen D. Q., “Central Limit Theorems for Mixing Arrays”, Vietnam Journal of Mathematics (to appear)

[14] Ziegler K., “On the asymptotic normality of kernel regression estimators of the mode in the nonparametric random design model”, J. Statist. Plann. Inf., 2003, no. 115, 123–144 | DOI | MR | Zbl