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dc.contributor.authorGenton, Marc G.
dc.contributor.authorRuiz-Gazen, Anne
dc.date.accessioned2016-02-28T06:44:23Z
dc.date.available2016-02-28T06:44:23Z
dc.date.issued2010-01
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.
dc.identifier.issn1061-8600
dc.identifier.issn1537-2715
dc.identifier.doi10.1198/jcgs.2010.09101
dc.identifier.urihttp://hdl.handle.net/10754/600174
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.
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.
dc.publisherInforma UK Limited
dc.subjectAutocovariance
dc.subjectMoran's I
dc.subjectOutlier
dc.subjectQuadratic form
dc.subjectRobustness
dc.subjectSpace
dc.subjectTime
dc.subjectVariogram
dc.titleVisualizing Influential Observations in Dependent Data
dc.typeArticle
dc.identifier.journalJournal of Computational and Graphical Statistics
dc.contributor.institutionTexas A and M University, College Station, United States
dc.contributor.institutionUniversite Toulouse 1 Sciences Sociales, Toulouse, France
kaust.grant.numberKUS-C1-016-04


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