The Annals of Statistics

Adaptive Root $n$ Estimates of Integrated Squared Density Derivatives

Tiee-Jian Wu

Full-text: Open access

Abstract

Based on a random sample of size $n$ from an unknown density $f$ on the real line, the nonparametric estimation of $\theta_k = \int\{f^{(k)}(x)\}^2 dx, k = 0, 1,\ldots$, is considered. These functionals are important in a number of contexts. The proposed estimates of $\theta_k$ is constructed in the frequency domain by using the sample characteristic function. It is known that the sample characteristic function at high frequency is dominated by sample variation and does not contain much information about $f$. Hence, the variation of the estimate can be reduced by modifying the sample characteristic function beyond some cutoff frequency. It is proposed to select adaptively the cutoff frequency by a generalization of the (smoothed) cross-validation. The exact convergence rate of the proposed estimate to $\theta_k$ is established. It depends solely on the smoothness of $f$. For sufficiently smooth $f$, it is shown that the proposed estimate is asymptotically normal, attains the optimal $O_p(n^{-1/2})$ rate and achieves the information bound. Finally, to improve the performance of the proposed estimate at small to moderately large $n$, two modifications are proposed. One modification is for estimating $\theta_0$; it reduces bias of the estimate. The other modification is for estimating $\theta_k, k \geq 1$; it reduces sample variation of the estimate. In simulation studies the superior performance of the proposed procedures is clearly demonstrated.

Article information

Source
Ann. Statist., Volume 23, Number 5 (1995), 1474-1495.

Dates
First available in Project Euclid: 11 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176324308

Digital Object Identifier
doi:10.1214/aos/1176324308

Mathematical Reviews number (MathSciNet)
MR1370292

Zentralblatt MATH identifier
0843.62036

JSTOR
links.jstor.org

Subjects
Primary: 62G05: Estimation
Secondary: 62G20: Asymptotic properties

Keywords
Density derivative nonparametric information bound characteristic function kernel estimate smoothed cross-validation bandwidth selection convergence rate

Citation

Wu, Tiee-Jian. Adaptive Root $n$ Estimates of Integrated Squared Density Derivatives. Ann. Statist. 23 (1995), no. 5, 1474--1495. doi:10.1214/aos/1176324308. https://projecteuclid.org/euclid.aos/1176324308


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