Handle URI:
http://hdl.handle.net/10754/598381
Title:
Functional Boxplots
Authors:
Sun, Ying; Genton, Marc G.
Abstract:
This article proposes an informative exploratory tool, the functional boxplot, for visualizing functional data, as well as its generalization, the enhanced functional boxplot. Based on the center outward ordering induced by band depth for functional data, the descriptive statistics of a functional boxplot are: the envelope of the 50% central region, the median curve, and the maximum non-outlying envelope. In addition, outliers can be detected in a functional boxplot by the 1.5 times the 50% central region empirical rule, analogous to the rule for classical boxplots. The construction of a functional boxplot is illustrated on a series of sea surface temperatures related to the El Niño phenomenon and its outlier detection performance is explored by simulations. As applications, the functional boxplot and enhanced functional boxplot are demonstrated on children growth data and spatio-temporal U.S. precipitation data for nine climatic regions, respectively. This article has supplementary material online. © 2011 American Statistical Association.
Citation:
Sun Y, Genton MG (2011) Functional Boxplots. Journal of Computational and Graphical Statistics 20: 316–334. Available: http://dx.doi.org/10.1198/jcgs.2011.09224.
Publisher:
Informa UK Limited
Journal:
Journal of Computational and Graphical Statistics
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
Jan-2011
DOI:
10.1198/jcgs.2011.09224
Type:
Article
ISSN:
1061-8600; 1537-2715
Sponsors:
This research was partially supported by NSF grants CMG ATM-0620624, DMS-1007504, and award no. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). The authors thank the editor, an associate editor, and three anonymous referees for their valuable comments.
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Full metadata record

DC FieldValue Language
dc.contributor.authorSun, Yingen
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2016-02-25T13:19:45Zen
dc.date.available2016-02-25T13:19:45Zen
dc.date.issued2011-01en
dc.identifier.citationSun Y, Genton MG (2011) Functional Boxplots. Journal of Computational and Graphical Statistics 20: 316–334. Available: http://dx.doi.org/10.1198/jcgs.2011.09224.en
dc.identifier.issn1061-8600en
dc.identifier.issn1537-2715en
dc.identifier.doi10.1198/jcgs.2011.09224en
dc.identifier.urihttp://hdl.handle.net/10754/598381en
dc.description.abstractThis article proposes an informative exploratory tool, the functional boxplot, for visualizing functional data, as well as its generalization, the enhanced functional boxplot. Based on the center outward ordering induced by band depth for functional data, the descriptive statistics of a functional boxplot are: the envelope of the 50% central region, the median curve, and the maximum non-outlying envelope. In addition, outliers can be detected in a functional boxplot by the 1.5 times the 50% central region empirical rule, analogous to the rule for classical boxplots. The construction of a functional boxplot is illustrated on a series of sea surface temperatures related to the El Niño phenomenon and its outlier detection performance is explored by simulations. As applications, the functional boxplot and enhanced functional boxplot are demonstrated on children growth data and spatio-temporal U.S. precipitation data for nine climatic regions, respectively. This article has supplementary material online. © 2011 American Statistical Association.en
dc.description.sponsorshipThis research was partially supported by NSF grants CMG ATM-0620624, DMS-1007504, and award no. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). The authors thank the editor, an associate editor, and three anonymous referees for their valuable comments.en
dc.publisherInforma UK Limiteden
dc.subjectDepthen
dc.subjectFunctional dataen
dc.subjectGrowth dataen
dc.subjectPrecipitation dataen
dc.subjectSpace-time dataen
dc.subjectVisualizationen
dc.titleFunctional Boxplotsen
dc.typeArticleen
dc.identifier.journalJournal of Computational and Graphical Statisticsen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
kaust.grant.numberKUS-C1-016-04en
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