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Activity Number: 132
Type: Contributed
Date/Time: Monday, August 4, 2014 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract #311033 View Presentation
Title: Uncertainty Measures and Limiting Distributions for Filament Estimation
Author(s): Yen-Chi Chen*+ and Christopher Genovese and Larry Wasserman
Companies: Carnegie Mellon and Carnegie Mellon and Carnegie Mellon
Keywords: filaments ; ridges ; manifold learning ; density estimation
Abstract:

A filament is a high density, connected region in a point cloud. There are several methods for estimating filaments but these methods do not provide any measure of uncertainty. We give a definition for the uncertainty of estimated filaments and we study statistical properties of the estimated filaments. We show how to estimate the uncertainty measures and we construct confidence sets based on a bootstrapping technique. We apply our methods to astronomy data and earthquake data.


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