Bounding uncertainty in functional data: A case study

King, Caleb; Martin, Nevin; Tucker, James Derek

doi:10.1080/08982112.2020.1851710

Bounding uncertainty in functional data: A case study

Journal Article · Thu Jan 28 23:00:00 EST 2021 · Quality Engineering

DOI:https://doi.org/10.1080/08982112.2020.1851710· OSTI ID:1783253

^[1]; Martin, Nevin ^[2]; ^[2]

SAS Institute, Cary, NC (United States). JMP Division
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Statistical Sciences

Functional data are fast becoming a preeminent source of information across a wide range of industries. A particularly challenging aspect of functional data is bounding uncertainty. In this unique case study, we present our attempts at creating bounding functions for selected applications at Sandia National Laboratories (SNL). The first attempt involved a simple extension of functional principal component analysis (fPCA) to incorporate covariates. Though this method was straightforward, the extension was plagued by poor coverage accuracy for the bounding curve. This led to a second attempt utilizing elastic methodology which yielded more accurate coverage at the cost of more complexity.

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: US Department of Homeland Security

Grant/Contract Number:: AC04-94AL85000; NA0003525

OSTI ID:: 1783253

Report Number(s):: SAND--2020-8137J; 689780

Journal Information:: Quality Engineering, Journal Name: Quality Engineering Journal Issue: 1 Vol. 33; ISSN 0898-2112

Publisher:: American Society for Quality ControlCopyright Statement

Country of Publication:: United States

Language:: English

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Related Subjects

97 MATHEMATICS AND COMPUTING
Functional Data Analysis
Functional Principal Components
Reliability Specifications
Tolerance Bounds
Uncertainty Quantification

Bounding uncertainty in functional data: A case study

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