The Annals of Statistics

Theoretical analysis of nonparametric filament estimation

Wanli Qiao and Wolfgang Polonik

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This paper provides a rigorous study of the nonparametric estimation of filaments or ridge lines of a probability density $f$. Points on the filament are considered as local extrema of the density when traversing the support of $f$ along the integral curve driven by the vector field of second eigenvectors of the Hessian of $f$. We “parametrize” points on the filaments by such integral curves, and thus both the estimation of integral curves and of filaments will be considered via a plug-in method using kernel density estimation. We establish rates of convergence and asymptotic distribution results for the estimation of both the integral curves and the filaments. The main theoretical result establishes the asymptotic distribution of the uniform deviation of the estimated filament from its theoretical counterpart. This result utilizes the extreme value behavior of nonstationary Gaussian processes indexed by manifolds $M_{h},h\in(0,1]$ as $h\to0$.

Article information

Ann. Statist., Volume 44, Number 3 (2016), 1269-1297.

Received: May 2014
Revised: October 2015
First available in Project Euclid: 11 April 2016

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

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

Extreme value distribution nonparametric curve estimation integral curves kernel density estimation


Qiao, Wanli; Polonik, Wolfgang. Theoretical analysis of nonparametric filament estimation. Ann. Statist. 44 (2016), no. 3, 1269--1297. doi:10.1214/15-AOS1405.

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Supplemental materials

  • Supplement to “Theoretical analysis of nonparametric filament estimation”. Due to page constraints on the main article, this supplement presents the proofs of some technical results in this paper as well as some miscellaneous results (Appendix B) that are used in the proofs.