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Title: Elastic depths for detecting shape anomalies in functional data

Abstract

Abstract not provided.

Authors:
 [1]; ORCiD logo [2];  [1];  [2]
  1. Univ. of Illinois at Urbana-Champaign, IL (United States)
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1650152
Alternate Identifier(s):
OSTI ID: 1670729
Report Number(s):
SAND-2020-8106J; SAND-2019-7579J
Journal ID: ISSN 0040-1706; 689760
Grant/Contract Number:  
AC04-94AL85000; NA0003525
Resource Type:
Accepted Manuscript
Journal Name:
Technometrics
Additional Journal Information:
Journal Name: Technometrics; Journal ID: ISSN 0040-1706
Publisher:
Taylor & Francis
Country of Publication:
United States
Language:
English
Subject:
Anomaly detection; Data depth; Functional data; Shape analysis

Citation Formats

Harris, Trevor, Tucker, J. Derek, Li, Bo, and Shand, Lyndsay. Elastic depths for detecting shape anomalies in functional data. United States: N. p., 2020. Web. https://doi.org/10.1080/00401706.2020.1811156.
Harris, Trevor, Tucker, J. Derek, Li, Bo, & Shand, Lyndsay. Elastic depths for detecting shape anomalies in functional data. United States. https://doi.org/10.1080/00401706.2020.1811156
Harris, Trevor, Tucker, J. Derek, Li, Bo, and Shand, Lyndsay. Wed . "Elastic depths for detecting shape anomalies in functional data". United States. https://doi.org/10.1080/00401706.2020.1811156. https://www.osti.gov/servlets/purl/1650152.
@article{osti_1650152,
title = {Elastic depths for detecting shape anomalies in functional data},
author = {Harris, Trevor and Tucker, J. Derek and Li, Bo and Shand, Lyndsay},
abstractNote = {Abstract not provided.},
doi = {10.1080/00401706.2020.1811156},
journal = {Technometrics},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {8}
}

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