The Annals of Statistics

Density Estimation Under Long-Range Dependence

Sandor Csorgo and Jan Mielniczuk

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Abstract

Dehling and Taqqu established the weak convergence of the empirical process for a long-range dependent stationary sequence under Gaussian subordination. We show that the corresponding density process, based on kernel estimators of the marginal density, converges weakly with the same normalization to the derivative of their limiting process. The phenomenon, which carries on for higher derivatives and for functional laws of the iterated logarithm, is in contrast with independent or weakly dependent situations, where the density process cannot be tight in the usual function spaces with supremum distances.

Article information

Source
Ann. Statist., Volume 23, Number 3 (1995), 990-999.

Dates
First available in Project Euclid: 11 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176324632

Digital Object Identifier
doi:10.1214/aos/1176324632

Mathematical Reviews number (MathSciNet)
MR1345210

Zentralblatt MATH identifier
0843.62037

JSTOR
links.jstor.org

Subjects
Primary: 62G07: Density estimation
Secondary: 62M99: None of the above, but in this section 60F17: Functional limit theorems; invariance principles

Keywords
Long-range dependence Gaussian subordination kernel density estimators weak convergence in supremum norm degenerate limiting processes Hermite polynomials

Citation

Csorgo, Sandor; Mielniczuk, Jan. Density Estimation Under Long-Range Dependence. Ann. Statist. 23 (1995), no. 3, 990--999. doi:10.1214/aos/1176324632. https://projecteuclid.org/euclid.aos/1176324632


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