Show simple item record

dc.contributor.authorYao, Zonghui
dc.contributor.authorDai, Wenlin
dc.contributor.authorGenton, Marc G.
dc.date.accessioned2020-11-03T10:31:38Z
dc.date.available2019-12-18T13:07:51Z
dc.date.available2020-11-03T10:31:38Z
dc.date.issued2020-07-03
dc.date.submitted2019-12-03
dc.identifier.citationYao, Z., Dai, W., & G. Genton, M. (2020). Trajectory functional boxplots. Stat, 9(1). doi:10.1002/sta4.289
dc.identifier.issn2049-1573
dc.identifier.doi10.1002/sta4.289
dc.identifier.urihttp://hdl.handle.net/10754/660679
dc.description.abstractWith the development of data-monitoring techniques in various fields of science, multivariate functional data are often observed. Consequently, an increasing number of methods have appeared to extend the general summary statistics of multivariate functional data. However, trajectory functional data, as an important subtype, have not been studied very well. This article proposes two informative exploratory tools: the trajectory functional boxplot and the modified simplicial band depth (MSBD) versus wiggliness of directional outlyingness (WO) plot, to visualize the centrality of trajectory functional data. The newly defined WO index effectively measures the shape variation of curves and hence serves as a detector for shape outliers; additionally, MSBD provides a centre-outward ranking and works as a detector for magnitude outliers. Using these two measures, the functional boxplot of the trajectory reveals centre-outward patterns and potential outliers using the raw curves, whereas the MSBD-WO plot illustrates such patterns and outliers in a space spanned by MSBD and WO. The proposed methods are validated on hurricane path data and migration trace data recorded from two types of birds.
dc.description.sponsorshipWe thank Dr. Donald H. House and his group at Clemson University for sharing the ensemble hurricane generator code. The research reported in this paper was supported by King Abdullah University of Science and Technology (KAUST).
dc.publisherWiley
dc.relation.urlhttps://onlinelibrary.wiley.com/doi/10.1002/sta4.289
dc.rightsArchived with thanks to Stat
dc.titleTrajectory functional boxplots
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics
dc.contributor.departmentStatistics Program
dc.identifier.journalStat
dc.rights.embargodate2021-04-21
dc.eprint.versionPost-print
dc.contributor.institutionInstitute of Statistics and Big Data, Renmin University of China, Beijing, 100872, China
dc.identifier.volume9
dc.identifier.issue1
dc.identifier.arxivid1904.10792
kaust.personYao, Zonghui
kaust.personG. Genton, Marc
dc.date.accepted2020-04-21
dc.identifier.eid2-s2.0-85094184904
refterms.dateFOA2019-12-18T13:08:53Z
dc.date.published-online2020-07-03
dc.date.published-print2020-12
dc.date.posted2019-04-24


Files in this item

Thumbnail
Name:
Preprintfile1.pdf
Size:
6.780Mb
Format:
PDF
Description:
Pre-print

This item appears in the following Collection(s)

Show simple item record

VersionItemEditorDateSummary

*Selected version