Abstract
We establish a uniform functional law of the logarithm for a Gaussian process closely related to the local empirical process. We then discuss the necessity of the polynomial covering assumption on the indexing class of functions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
K.S. AlexanderRates of growth and sample moduli for weighted empirical processes. Probab. Th. Rel. Fields 75 (1987), 379–423.
M. ArconesThe large deviation principle of stochastic processes. I. Theor. Probab. Appl.47 (2002a). In press.
M. ArconesThe large deviation principle of stochastic processes. II.Theor. Probab. Appl. 47 (2002b). In press.
M. Csörgö and P. RévészHow big are the increments of the Wiener process?Ann. Probab. 7 (1979), 731–737.
M. Csörgö and P. RévészStrong Approximations in Probability and Statistics. Academic Press, New York, 1981.
P. DeheuvelsFunctional laws of the iterated logarithm for large increments of empirical and quantile processes. Stoch. Proc. Appl.43 (1992), 133–163
P. Deheuvels and D.M. MasonFunctional laws of the iterated logarithm for increments of empirical and quantile processes. Ann. Probab. 20 (1992), 1248–1287.
P. Deheuvels and D.M. MasonFunctional laws of the iterated logarithm for local empirical processes indexed by sets. Ann. Probab. 22 (1994), 1619–1661.
L. Devroye and G. LugosiCombinatorial Methods in Density Estimation. Springer, New York, 2000.
R. M. DudleyUniform Central Limit TheoremsCambridge University Press, New York, 1999.
U. Einmahl and D.M. MasonGaussian approximation of local empirical processes indexed by functions. Probab. Th. Rel. Fields 107 (1997), 283–311.
U. Einmahl and D.M. MasonStrong approximations for local empirical processes. In: Progress in Probability 43, Proceedings of High Dimensional Probability, Oberwolfach 1996(E. Eberlein, M. Hahn and J. Kuelbs, eds.), pp. 75–92, Birkhäuser, Basel, 1998.
U. Einmahl and D.M. MasonAn empirical process approach to the uniform consistency of kernel—type function estimators. J. Theoretical Prob. 13 (2000), 1–37.
E. Giné and A. GuillouRates of strong consistency for multivariate kernel density estimators. Ann. Inst. H. Poincaré. 38 (2002), 907–921.
E. Giné, V. Koltchinskii and J. Wellner, Ratio limit theorems for empirical processes. Preprint.
K. ItôMultiple Wiener integral. J. Math. Soc. Japan 3 (1951),157–169.
J. Komlós, P. Major and G. TusnádyAn approximation of partial sums of independent rv’s and the sample df I. Z. Wahrsch. verw. Gebiete. 32 (1975), 111–131.
J. Komlós, P. Major and G. TusnádyAn approximation of partial sums of independent rv’s and the sample df II. Z. Wahrsch. verw. Gebiete. 34 (1976), 33–58.
M. Ledoux and M. Talagrand, Probability in Banach Spaces, Springer, New York, 1991.
D.M. MasonA strong invariance theorem for the tail empirical process. Ann. Inst. H. Poincaré 24 (1988), 491–506.
D.M. Mason, A Uniform functional law of the logarithm for the local empirical process. Preprint.
D.M. Mason, G.R. Shorack and J.A. WellnerStrong limit theorems for the oscillation moduli of the uniform empirical process. Z. Wahrsch. verw. Gebiete 65 (1983), 83–97.
C. MuellerA unification of Strassen’s law and Levy’s modulus of continuity. Z.Wahrsch. 56 (1981), 163–179.
D. Nolan and J.S. MarronUniform consistency of automatic and location—adaptive delta—sequence estimators. Probab. Th. Rel. Fields 80 (1989), 619–632.
D. Nolan and D. PollardU—processes: rates of convergence. Ann. Statist. 15 (1987), 780–799.
E. ParzenAn approach to time series analysis. Ann. Math. Statist. 32 (1961), 951–989.
P. RévészA generalization of Strassen’s functional law of the iterated logarithm. Wahrsch. verw. Gebiete. 50 (1979), 257–264.
E. RioLocal invariance principles and their applications to density estimation. Probab. Th. Rel. Fields 98 (1994), 21–45.
G.R. Shorack and J.A. WellnerEmpirical Processes with Applications to Statistics. Wiley1986.
E.M. SteinSingular Integrals and Differentiability Properties of Functions. Princeton University Press, Princeton, New Jersey, 1970.
W. StuteThe oscillation behavior of empirical processes. Ann. Probab. 10 (1982a), 86–107.
W. StuteThe law of the iterated logarithm for kernel density estimators. Ann. Probab. 10 (1982b), 414–422.
W. StuteThe oscillation behavior of empirical processes: the multivariate case. Ann. Probab. 12 (1984), 361–379.
M. TalagrandSharper bounds for Gaussian and empirical processes. Ann. Probab. (1994), 28–76.
A.W. van der Vaart and J.A. Wellner, Weak Convergence and Empirical Processes. Springer, New York, 1996.
A.Yu ZaitsevEstimates of the Lévy—Prokhorov distance in the multivariate central limit theorem for random variables with finite exponential moments. Theory Probab. Appl. 31 (1987a), 203–220.
A.Yu ZaitsevOn the Gaussian approximation of convolutions under multidimensional analogues of S. N. Bernstein’s inequality conditions. Probab. Th. Rel. Fields 74 (1987b), 534–566.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer Basel AG
About this paper
Cite this paper
Mason, D.M. (2003). A Uniform Functional Law of the Logarithm for a Local Gaussian Process. In: Hoffmann-Jørgensen, J., Wellner, J.A., Marcus, M.B. (eds) High Dimensional Probability III. Progress in Probability, vol 55. Birkhäuser, Basel. https://doi.org/10.1007/978-3-0348-8059-6_9
Download citation
DOI: https://doi.org/10.1007/978-3-0348-8059-6_9
Publisher Name: Birkhäuser, Basel
Print ISBN: 978-3-0348-9423-4
Online ISBN: 978-3-0348-8059-6
eBook Packages: Springer Book Archive