Source: Ann. Appl. Stat.
Volume 3, Number 4
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
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