The Annals of Statistics

Asymptotic theory for density ridges

Yen-Chi Chen, Christopher R. Genovese, and Larry Wasserman

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The large sample theory of estimators for density modes is well understood. In this paper we consider density ridges, which are a higher-dimensional extension of modes. Modes correspond to zero-dimensional, local high-density regions in point clouds. Density ridges correspond to $s$-dimensional, local high-density regions in point clouds. We establish three main results. First we show that under appropriate regularity conditions, the local variation of the estimated ridge can be approximated by an empirical process. Second, we show that the distribution of the estimated ridge converges to a Gaussian process. Third, we establish that the bootstrap leads to valid confidence sets for density ridges.

Article information

Ann. Statist., Volume 43, Number 5 (2015), 1896-1928.

Received: June 2014
Revised: March 2015
First available in Project Euclid: 3 August 2015

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62G20: Asymptotic properties
Secondary: 62G15: Tolerance and confidence regions 62G05: Estimation

Ridge density estimation nonparametric statistics empirical process bootstrap


Chen, Yen-Chi; Genovese, Christopher R.; Wasserman, Larry. Asymptotic theory for density ridges. Ann. Statist. 43 (2015), no. 5, 1896--1928. doi:10.1214/15-AOS1329.

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