Gervini, D. (2009). Detecting and handling outlying trajectories in irregularly sampled functional datasets. The Annals of Applied Statistics 3 , 1758–1775. He, X., Simpson, D. G. and Wang, G. (2000). Breakdown points of t -type regression estimators. Biometrika 87 , 675–687. James, G., Hastie, T. G. and Sugar, C. A. (2000). Principal component models for sparse functional data. Biometrika 87 , 587–602. Locantore, N., Marron, J. S., Simpson, D. G., Tripoli, N., Zhang, J. T. and Cohen, K. L. (1999). Robust principal components for functional data (with discussion). Test 8 , 1–28. Magnus, J. R., and Neudecker, H. (1999). Matrix Differential Calculus with Applications in Statistics and Econometrics. Revised Edition , New York: Wiley. Maronna, R. A., Martin, R. D. and Yohai, V. J. (2006). Robust Statistics. Theory and Methods . New York: Wiley. Maronna, R. A. and Yohai, V. J. (2012). Robust functional linear regression based on splines. To appear in Computational Statistics & Data Analysis. M¨uller, H.-G., Chiou, J.-M., and Leng, X. (2008). Inferring gene expression dynamics via functional regression analysis. BMC Bioinformatics 9:60 . Ramsay, J. O. and Silverman, B. W. (2005). Functional Data Analysis. Second Edition . New York: Springer. Van der Vaart, A. W. (1998). Asymptotic Statistics . Cambridge Series in Statistical and Probabilistic Mathematics, Cambridge, UK: Cambridge University Press. 22