Functional outlier detection with robust functional principal component analysis |
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Authors: | Pallavi Sawant Nedret Billor Hyejin Shin |
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Institution: | (1) Tarumanagara University, Jln Let. Jend. S Parman 1, Jakarta, 1140, Indonesia |
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Abstract: | Functional principal component analysis is the preliminary step to represent the data in a lower dimensional space and to
capture the main modes of variability of the data by means of small number of components which are linear combinations of
original variables. Sensitivity of the variance and the covariance functions to irregular observations make this method vulnerable
to outliers and may not capture the variation of the regular observations. In this study, we propose a robust functional principal
component analysis to find the linear combinations of the original variables that contain most of the information, even if
there are outliers and to flag functional outliers. We demonstrate the performance of the proposed method on an extensive
simulation study and two datasets from chemometrics and environment. |
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Keywords: | |
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