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Activity Number: 133 - Statistical Methods for Functional Data
Type: Contributed
Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract #304338 Presentation
Title: A New Metric for Estimating Noise in Functional Data
Author(s): Subhrangshu Nandi* and Michael Abott Newton
Companies: Amazon and University of Wisconsin - Madison
Keywords: functional data; noise; genomics

In functional data analysis, where both amplitude and phase variability affect individual trajectories, noise measures like pointwise variance are not appropriate because adjacent points are not independent of each other. While there exists techniques for estimating similarity between curves, there is no measure for quantifying noise in a sample of curves. We propose a novel method of quantifying the noise in a sample of noisy realizations of an underlying smooth function. The noise can be in either of the two axes. We use this measure in the analysis of gray-level intensity measurements derived from large-scale experiments involving long DNA molecules where we develop an “iterated curve registration” technique to extract signal from very noisy data.

Authors who are presenting talks have a * after their name.

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