Open Access
December 2009 Detecting and handling outlying trajectories in irregularly sampled functional datasets
Daniel Gervini
Ann. Appl. Stat. 3(4): 1758-1775 (December 2009). DOI: 10.1214/09-AOAS257


Outlying curves often occur in functional or longitudinal datasets, and can be very influential on parameter estimators and very hard to detect visually. In this article we introduce estimators of the mean and the principal components that are resistant to, and then can be used for detection of, outlying sample trajectories. The estimators are based on reduced-rank t-models and are specifically aimed at sparse and irregularly sampled functional data. The outlier-resistance properties of the estimators and their relative efficiency for noncontaminated data are studied theoretically and by simulation. Applications to the analysis of Internet traffic data and glycated hemoglobin levels in diabetic children are presented.


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Daniel Gervini. "Detecting and handling outlying trajectories in irregularly sampled functional datasets." Ann. Appl. Stat. 3 (4) 1758 - 1775, December 2009.


Published: December 2009
First available in Project Euclid: 1 March 2010

zbMATH: 1184.62101
MathSciNet: MR2752157
Digital Object Identifier: 10.1214/09-AOAS257

Keywords: Functional data analysis , influence function , latent variable models , longitudinal data analysis , Principal Component Analysis

Rights: Copyright © 2009 Institute of Mathematical Statistics

Vol.3 • No. 4 • December 2009
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