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CPS2526 Holger Cevallos-Valdiviezo et al.
4. Discussion and Conclusion
Simulation studies showed that CooLTS and MVLTS yield competitive
results when compared to existing methods when a minority of observations
is contaminated. When a majority of the curves is contaminated at some
positions along its trajectory coordinatewise methods like Coordinatewise LTS
are preferred over multivariate LTS and other multivariate methods since they
break down in this case.

References
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2. Boente, G. & Salibian-Barrera, M. (2015). S-estimators for functional
principal component analysis. Journal of the American Statistical
Association, 110(511), 1100-1111.
3. Cevallos-Valdiviezo, H. & Van Aelst, S. (2019). Fast computation of
robust subspace estimators. Computational Statistics & Data Analysis,
134, 171-185.
4. Maronna, R. A. (2005). Principal Components and Orthogonal Regression
Based on Robust Scales. Technometrics, 47, 264-273.

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