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On a Gaussian process related to multivariate probability density estimation

Published online by Cambridge University Press:  24 October 2008

B. W. Silverman
B. W. Silverman
Affiliation:
Statistical Laboratory, University of Cambridge
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The multivariate Gaussian process with the same variance/covariance structure as the multivariate kernel density estimator in Euclidean space of dimension d is considered. An exact result is obtained for the limit in probability of the maximum of the normalized process. In addition weak and strong bounds are placed on the asymptotic behaviour of the maximum of the process over a multidimensional interval which is allowed to increase as the sample size increases. All the bounds obtained on the process are

Only the uniform continuity of the underlying density is assumed; the conditions on the kernel are also mild.

Information

Type
Research Article
Information
Mathematical Proceedings of the Cambridge Philosophical Society , Volume 80 , Issue 1 , July 1976 , pp. 135 - 144
Copyright
Copyright © Cambridge Philosophical Society 1976

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References

REFERENCES

(1)Cramer, H. and Leadbetter, M. R.Stationary and related stochastic processes. (New York: J. Wiley & Sons, Inc., 1967.)Google Scholar
(2)Garsia, A. M. Continuity properties of Gaussian processes with multidimensional time parameter. Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability, volume II. (Berkeley: University of California Press, 1972), 369–374.Google Scholar
(3)Revesz, P. On multivariate empirical density functions. (To appear.)Google Scholar
(4)Rosenblatt, M.Remarks on some non-parametric estimates of a density function. Ann. Math. Statist. 27 (1956), 832–837.CrossRefGoogle Scholar
(5)Rosenblatt, M.A quadratic measure of deviation of two-dimensional density estimates and a test of independence. Ann. Statist. 3 (1975), 1–14.CrossRefGoogle Scholar