Handle URI:
http://hdl.handle.net/10754/600174
Title:
Visualizing Influential Observations in Dependent Data
Authors:
Genton, Marc G.; Ruiz-Gazen, Anne
Abstract:
We introduce the hair-plot to visualize influential observations in dependent data. It consists of all trajectories of the value of an estimator when each observation is modified in turn by an additive perturbation. We define two measures of influence: the local influence which describes the rate of departure from the original estimate due to a small perturbation of each observation; and the asymptotic influence which indicates the influence on the original estimate of the most extreme contamination for each observation. The cases of estimators defined as quadratic forms or ratios of quadratic forms are investigated in detail. Sample autocovariances, covariograms, and variograms belong to the first case. Sample autocorrelations, correlograms, and indices of spatial autocorrelation such as Moran's I belong to the second case.We illustrate our approach on various datasets from time series analysis and spatial statistics. This article has supplementary material online. © 2010 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.
Citation:
Genton MG, Ruiz-Gazen A (2010) Visualizing Influential Observations in Dependent Data. Journal of Computational and Graphical Statistics 19: 808–825. Available: http://dx.doi.org/10.1198/jcgs.2010.09101.
Publisher:
Informa UK Limited
Journal:
Journal of Computational and Graphical Statistics
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
Jan-2010
DOI:
10.1198/jcgs.2010.09101
Type:
Article
ISSN:
1061-8600; 1537-2715
Sponsors:
Genton's research was supported in part by NSF grants CMG ATM-0620624 and DMS-1007504, and by award no. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). The authors are grateful to Thibault Laurent for implementing the hair-plot function in R and thank the editor, an associate editor, and two anonymous referees for their valuable comments.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorGenton, Marc G.en
dc.contributor.authorRuiz-Gazen, Anneen
dc.date.accessioned2016-02-28T06:44:23Zen
dc.date.available2016-02-28T06:44:23Zen
dc.date.issued2010-01en
dc.identifier.citationGenton MG, Ruiz-Gazen A (2010) Visualizing Influential Observations in Dependent Data. Journal of Computational and Graphical Statistics 19: 808–825. Available: http://dx.doi.org/10.1198/jcgs.2010.09101.en
dc.identifier.issn1061-8600en
dc.identifier.issn1537-2715en
dc.identifier.doi10.1198/jcgs.2010.09101en
dc.identifier.urihttp://hdl.handle.net/10754/600174en
dc.description.abstractWe introduce the hair-plot to visualize influential observations in dependent data. It consists of all trajectories of the value of an estimator when each observation is modified in turn by an additive perturbation. We define two measures of influence: the local influence which describes the rate of departure from the original estimate due to a small perturbation of each observation; and the asymptotic influence which indicates the influence on the original estimate of the most extreme contamination for each observation. The cases of estimators defined as quadratic forms or ratios of quadratic forms are investigated in detail. Sample autocovariances, covariograms, and variograms belong to the first case. Sample autocorrelations, correlograms, and indices of spatial autocorrelation such as Moran's I belong to the second case.We illustrate our approach on various datasets from time series analysis and spatial statistics. This article has supplementary material online. © 2010 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.en
dc.description.sponsorshipGenton's research was supported in part by NSF grants CMG ATM-0620624 and DMS-1007504, and by award no. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). The authors are grateful to Thibault Laurent for implementing the hair-plot function in R and thank the editor, an associate editor, and two anonymous referees for their valuable comments.en
dc.publisherInforma UK Limiteden
dc.subjectAutocovarianceen
dc.subjectMoran's Ien
dc.subjectOutlieren
dc.subjectQuadratic formen
dc.subjectRobustnessen
dc.subjectSpaceen
dc.subjectTimeen
dc.subjectVariogramen
dc.titleVisualizing Influential Observations in Dependent Dataen
dc.typeArticleen
dc.identifier.journalJournal of Computational and Graphical Statisticsen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
dc.contributor.institutionUniversite Toulouse 1 Sciences Sociales, Toulouse, Franceen
kaust.grant.numberKUS-C1-016-04en
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